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Most organizations today have made the same bet.

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They've implemented co-pilot or built a chatbot

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or deployed some flavor of AI assistant

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that lets employees ask questions and get answers back.

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It works, employees use it, it feels productive,

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but here's what they don't see yet.

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That initial success is masking a structural problem.

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The problem isn't that co-pilot is bad.

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The problem is that chat is incomplete.

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Treating AI as a series of isolated conversational interfaces,

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one agent here, another tool there, a workflow somewhere else,

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feels fast because you're shipping individual pieces,

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but as you add more agents, more integrations and more autonomy,

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the hidden costs start appearing governance-debt.

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You've got agents borrowing human identities

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because nobody set up proper identity infrastructure.

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You've got models being called directly by every team

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because there's no central gateway.

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You've got data moving across regions and clouds

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with no policy enforcement.

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You've got agents still running two years

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after their original project ended,

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consuming tokens nobody's paying attention to.

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You've got no way to answer the question,

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which agent accessed this data and when.

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This is the moment most organizations realize

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that chat isn't a platform.

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It's a feature sitting on top of a missing architecture,

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the shift from co-pilot to agent mesh,

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from chat to agent fabric,

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requires building four structural layers

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that most organizations skip.

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You need a traceable identity plane.

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You need a reasoning layer that understands business logic

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instead of guessing at documents.

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You need a governance hub that centralizes policy enforcement

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and you need observability that lets you trace

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every action back to its source.

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Without these layers, you're not scaling AI.

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You're scaling risk.

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The co-pilot illusion.

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Let's be clear about what co-pilot is

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because the confusion starts here.

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Co-pilot is a feature.

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It's an incredibly useful feature.

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A conversational interface layered on top of a language model.

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It feels like you're building an AI platform

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because it's easy to stand up, easy to show results with

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and easy to get executives excited about.

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You spin it up on a Friday, show it to your team on Monday

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and suddenly everyone's talking about AI transformation,

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but a feature is not a platform.

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A platform is what you need to actually run agents at scale.

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A platform is the infrastructure

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that lets you deploy govern monitor

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and retire dozens or hundreds of agents without creating chaos.

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Here's the seduction.

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In isolation, co-pilot works beautifully.

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One team builds an agent for customer support.

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It integrates with their knowledge base.

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It handles routine questions

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and it escalates complex issues.

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Everyone's happy.

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Token consumption is low.

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Nobody's asking hard governance questions

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and the team ships fast.

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Then another team wants an agent.

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Then another suddenly you've got a support agent,

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a finance agent, a sales agent, and a recruiting agent.

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They're built in different places.

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Some in co-pilot studio, some in Azure AI, some in Foundry.

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Each team chose their own approach

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because there was no central architecture to guide them.

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And that's when the seam starts showing.

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The support agent is borrowing the IT manager's identity

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because nobody defined what an agent identity should look like.

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The finance agent is calling Azure OpenAI directly

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because there's no gateway.

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The sales agent is accessing data

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from multiple regions without any policy enforcement.

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The recruiting agent is still running

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on the same credentials the original builder set up three years ago.

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Nobody knows which agent is consuming the most tokens.

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Nobody knows which agent has access to which data.

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Nobody knows what happens

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if one of them starts behaving badly.

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This is the scaling problem that co-pilot doesn't solve.

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When you're running one agent, governance feels optional.

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When you're running 50, governance becomes survival.

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And by then your retrofitting controls onto a system

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that was never built to support them.

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The real cost isn't in the technology.

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It's in the organizational chaos.

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Finance runs their own model access

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because IT was too slow, marketing integrates their own tools

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because they couldn't get on the central catalog.

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Sales builds agents that move data across clouds

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because nobody enforced regional policies.

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And when compliance comes knocking

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with a question about which agent accessed this customer record,

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you don't have an answer.

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You can't trace it.

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You can't audit it.

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You can't even shut it down

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without breaking the process it was supporting.

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This is governance debt.

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You're borrowing control against future certainty

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that you won't have it.

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The hidden assumption underneath co-pilot,

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the one nobody voices but everyone makes,

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is that chat interfaces are governance enough

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that if employees can ask questions and get answers,

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that's platform behavior.

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But chat has no identity layer.

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Chat has no policy enforcement.

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Chat has no reasoning about business logic.

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Chat is stateless.

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Each conversation is isolated

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with no understanding of the full context.

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Chat scales to chaos remarkably fast.

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The move from co-pilot to agent mesh

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isn't about replacing co-pilot.

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It's about building the architecture

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that co-pilot should have been sitting on top of from the start.

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And that architecture starts

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with the problem most organizations don't even know they have.

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The moment your first agent logs in,

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you need to answer the question of who,

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or what is actually logging in, the identity problem.

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That question has a name.

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It's called identity.

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And identity is where most agent architectures fall apart.

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Here is what happens in practice.

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A team builds an agent.

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It needs to access data, call APIs, or trigger workflows.

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So someone asks a simple question,

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what account should this agent use?

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The answer is usually whatever is easiest,

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a shared service account,

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a developer's personal login.

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Sometimes they just use the identity of whoever

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creates the agent and leave it that way forever.

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The thinking is simple, the agent needs to do work

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so you give it credentials that let it do work problem solved.

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Except the problem isn't solved, it's hidden.

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The moment you do this, you create an accountability void.

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An agent running under a shared service account

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looks exactly like a human running under that same account.

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When that agent accesses customer data at 3 AM

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and moves it somewhere it shouldn't,

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your logs will show that the account access the data.

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But you cannot see that an agent did it.

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You cannot trace the decision back to the logic of the system.

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You cannot understand what it was trying to do.

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You cannot even prove it was an automated system

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and not the person whose credentials it's using.

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Now, scale that problem.

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You have 30 agents running under five shared accounts.

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One of them is accessing financial records

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without authorization, which one?

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When? Why?

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The audit log shows the account name and nothing else.

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The person who owns that account is confused

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because they don't remember authorizing that access.

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The developer who built the offending agent

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left the company months ago.

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You are stuck trying to reverse engineer a crisis

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by looking at a timestamp and a shared password.

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This is when regulators start asking hard questions.

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GDPR says you need to know who accessed personal data.

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ASOKI2 says you need to demonstrate access controls.

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HIPPA says you need to prove that only authorized systems

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touched health information.

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Your audit trail shows a service account.

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It doesn't show an agent.

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It doesn't show intent.

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It doesn't show whether the access was a legitimate task

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or a massive mistake.

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You cannot pass an audit with that.

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But there is a deeper structural reason identity matters.

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Agents are not passive tools.

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They are autonomous systems.

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They make decisions.

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They execute actions.

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They access data.

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And when something goes wrong,

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you need to know more than just what happened.

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You need to know why it happened.

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You need to trace back through the decision-making chain

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to see what data it saw, what policies it followed,

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and what its reasoning was.

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If the agent is hiding behind a human identity,

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that trace is broken.

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You lose the distinction between what a person authorized

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and what an agent decided to do on its own.

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The solution is what EntraID calls an agent ID.

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It is an identity object in your directory,

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just like a user or an app registration,

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but it is built specifically for agents.

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Each agent gets its own identity.

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Each agent has its own permissions,

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scope to exactly what it needs.

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Each action is logged under the agent's own name

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instead of being borrowed from a human.

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When that finance agent accesses a record at 3am,

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the log shows exactly which agent did it.

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You can trace the event back to the configuration,

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the permissions and the approval chain.

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You can ask if this agent was authorized to touch this data

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and the answer is a clear yes or no.

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That isn't just compliance.

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That's control.

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The moment you have traceable identities,

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the model changes.

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You can audit what agents are doing.

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You can enforce lease privilege

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so each agent gets only the permissions it requires.

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You can revoke access instantly

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if an agent misbehaves.

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You finally understand the full chain of accountability

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from who created the agent to what data it touches.

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Without it, you're flying blind, the reasoning floor,

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identity solved one problem,

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but it didn't solve the problem you actually hit

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when you try to make an agent useful.

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That problem is thinking.

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Here is a scenario that plays out in organizations every day.

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A team builds an agent to answer questions

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about company policy.

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Someone asks, what is our revenue recognition policy?

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The agent processes the question,

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searches through documents,

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finds something relevant and generates an answer.

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It looks right, it sounds confident.

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The person who asked the question

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reads the answer and acts on it.

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Six months later, that decision cost the company money

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because the agent was wrong.

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What actually happened?

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The agent found a document.

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It searched for text related to revenue and policy.

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It stitched together fragments

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that seemed to match the words in the question.

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It generated something plausible.

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But it had no idea if the answer was correct

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because it wasn't thinking about revenue recognition

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as a business concept.

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It was matching text patterns.

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This is what happens when you build agents on top of Ragn.

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Retrieval augmented generation is a technique

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where you take a question, search a document store

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for passages and feed those to a model.

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The goal is to ground the answer in your actual data

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instead of letting the model hallucinate.

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In theory, this solves the hallucination problem.

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In practice, Ragn just changes the shape of the hallucination

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because Ragn is document-centric.

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It treats your knowledge base as a pile of text chunks.

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When someone asks the question,

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the system searches for words that look relevant

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and hands them to the model.

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Then the model tries to guess what the question means

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and builds an answer from the fragments it was given.

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But text fragments are not meaning.

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They are just words.

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An agent that answers a policy question

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by retrieving a document is not understanding the policy,

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it is pattern matching against text.

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It doesn't know that revenue policy connects

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00:08:56,000 --> 00:08:58,680
to sales processes, accounting systems, contract terms

271
00:08:58,680 --> 00:08:59,720
and regional regulations.

272
00:08:59,720 --> 00:09:02,280
It doesn't know which policies contradict each other

273
00:09:02,280 --> 00:09:04,720
or which one takes precedence in a specific case.

274
00:09:04,720 --> 00:09:07,080
It doesn't know the business logic underneath the words.

275
00:09:07,080 --> 00:09:09,720
So it hallucinates.

276
00:09:09,720 --> 00:09:11,640
Not in the way people usually think.

277
00:09:11,640 --> 00:09:13,280
It isn't making up facts from thin air.

278
00:09:13,280 --> 00:09:14,880
It is doing something much more dangerous.

279
00:09:14,880 --> 00:09:16,400
It is taking real text fragments

280
00:09:16,400 --> 00:09:18,520
and assembling them into something that sounds coherent

281
00:09:18,520 --> 00:09:21,640
but has no relationship to how your business actually works.

282
00:09:21,640 --> 00:09:23,720
The gap here is the gap between finding text

283
00:09:23,720 --> 00:09:25,040
and understanding context.

284
00:09:25,040 --> 00:09:27,680
The solution is what Microsoft calls fabric ontologies.

285
00:09:27,680 --> 00:09:29,320
And it is a completely different way of thinking

286
00:09:29,320 --> 00:09:30,880
about how agents reason.

287
00:09:30,880 --> 00:09:33,280
Instead of treating your knowledge as a document store,

288
00:09:33,280 --> 00:09:35,400
you treat it as a model of your business.

289
00:09:35,400 --> 00:09:37,360
You define the entities that matter.

290
00:09:37,360 --> 00:09:40,840
Customer, order, product, root, asset.

291
00:09:40,840 --> 00:09:42,360
You define how they relate to each other.

292
00:09:42,360 --> 00:09:44,080
You define the rules that govern them.

293
00:09:44,080 --> 00:09:45,560
A customer places orders.

294
00:09:45,560 --> 00:09:48,320
Orders contain products, products belong to categories.

295
00:09:48,320 --> 00:09:51,960
Root service regions, assets break down and need maintenance.

296
00:09:51,960 --> 00:09:53,560
Then when an agent needs to answer a question,

297
00:09:53,560 --> 00:09:54,840
it doesn't search for text.

298
00:09:54,840 --> 00:09:56,680
It traverses that model.

299
00:09:56,680 --> 00:09:58,080
When asked about revenue policy,

300
00:09:58,080 --> 00:10:00,320
the agent understands revenue as a concept

301
00:10:00,320 --> 00:10:01,720
connected to orders and products.

302
00:10:01,720 --> 00:10:03,560
It understands policy as a rule set

303
00:10:03,560 --> 00:10:05,640
connected to contract types and regions.

304
00:10:05,640 --> 00:10:07,520
It can reason across those relationships

305
00:10:07,520 --> 00:10:09,920
instead of guessing at text fragments.

306
00:10:09,920 --> 00:10:11,880
The difference is structural.

307
00:10:11,880 --> 00:10:14,280
Rags says, find the words that match.

308
00:10:14,280 --> 00:10:17,760
Ontologies say, find the entities and relationships that matter.

309
00:10:17,760 --> 00:10:20,280
With Rags, an agent is just a sophisticated search

310
00:10:20,280 --> 00:10:21,320
and copy machine.

311
00:10:21,320 --> 00:10:23,280
With ontologies, an agent becomes something

312
00:10:23,280 --> 00:10:25,320
that actually reasons about your business.

313
00:10:25,320 --> 00:10:27,240
This is where the thinking happens.

314
00:10:27,240 --> 00:10:29,120
The governance debt accumulates.

315
00:10:29,120 --> 00:10:30,800
Identity and reasoning only matter

316
00:10:30,800 --> 00:10:32,680
if you can actually manage what you've built.

317
00:10:32,680 --> 00:10:33,960
But for most organizations,

318
00:10:33,960 --> 00:10:35,800
that's where the architecture breaks down.

319
00:10:35,800 --> 00:10:37,160
Here is what typically happens.

320
00:10:37,160 --> 00:10:39,960
You've solved the chat problem, you have agents working.

321
00:10:39,960 --> 00:10:41,320
Identity is starting to make sense.

322
00:10:41,320 --> 00:10:43,520
But now you have three different teams

323
00:10:43,520 --> 00:10:44,760
that all want to build agents.

324
00:10:44,760 --> 00:10:46,000
They all need to connect to models.

325
00:10:46,000 --> 00:10:47,080
They all need policies.

326
00:10:47,080 --> 00:10:48,160
They all need logging.

327
00:10:48,160 --> 00:10:50,080
And each team does what makes sense to them.

328
00:10:50,080 --> 00:10:52,440
The support team goes directly to Azure OpenAI

329
00:10:52,440 --> 00:10:53,920
because they need fast answers

330
00:10:53,920 --> 00:10:55,920
and don't want to wait for central approval.

331
00:10:55,920 --> 00:10:59,320
So they set up their own keys and monitoring and cost tracking.

332
00:10:59,320 --> 00:11:01,520
The finance team uses co-pilot studio

333
00:11:01,520 --> 00:11:03,240
because that's what they already know

334
00:11:03,240 --> 00:11:05,080
and they use it to build out their own version

335
00:11:05,080 --> 00:11:06,640
of workflow automation.

336
00:11:06,640 --> 00:11:08,960
The data team experiments with open source models

337
00:11:08,960 --> 00:11:10,600
because they want more control

338
00:11:10,600 --> 00:11:12,880
which leads them to spin up their own separate infrastructure.

339
00:11:12,880 --> 00:11:13,880
Nobody coordinated.

340
00:11:13,880 --> 00:11:14,880
Nobody asked for permission.

341
00:11:14,880 --> 00:11:16,360
Nobody followed a standard.

342
00:11:16,360 --> 00:11:17,560
And suddenly you have a problem

343
00:11:17,560 --> 00:11:20,120
that identity and reasoning alone cannot fix

344
00:11:20,120 --> 00:11:22,320
because you have no idea what is actually running.

345
00:11:22,320 --> 00:11:23,400
This is shadow AI.

346
00:11:23,400 --> 00:11:24,200
It isn't malicious.

347
00:11:24,200 --> 00:11:25,720
It's just what happens when governance

348
00:11:25,720 --> 00:11:27,960
doesn't exist at the point where decisions get made.

349
00:11:27,960 --> 00:11:28,880
Teams need to ship.

350
00:11:28,880 --> 00:11:30,200
They need to move fast.

351
00:11:30,200 --> 00:11:31,560
Central processes are slow.

352
00:11:31,560 --> 00:11:32,720
So they go around them.

353
00:11:32,720 --> 00:11:34,400
They configure their own model access.

354
00:11:34,400 --> 00:11:36,360
They set up their own approval workflows

355
00:11:36,360 --> 00:11:37,880
or they just skip them entirely.

356
00:11:37,880 --> 00:11:39,360
They implement their own logging

357
00:11:39,360 --> 00:11:40,960
or they don't log anything at all.

358
00:11:40,960 --> 00:11:44,400
The result is that your organization now has multiple control planes.

359
00:11:44,400 --> 00:11:46,720
Or more accurately, it has no control plane.

360
00:11:46,720 --> 00:11:48,280
The support team's direct integration

361
00:11:48,280 --> 00:11:51,320
has no relationship to what finance is doing in co-pilot studio

362
00:11:51,320 --> 00:11:53,400
and the data team's open source setup

363
00:11:53,400 --> 00:11:55,600
has no connection to either of them.

364
00:11:55,600 --> 00:11:56,720
You have three different teams

365
00:11:56,720 --> 00:11:57,960
using three different models

366
00:11:57,960 --> 00:11:59,920
with three different sets of tools and policies

367
00:11:59,920 --> 00:12:03,080
and there is no governance layer that understands how they relate.

368
00:12:03,080 --> 00:12:05,520
If the data team's model needs to access data

369
00:12:05,520 --> 00:12:07,480
that the support team is currently monitoring,

370
00:12:07,480 --> 00:12:10,120
there is no central policy to say whether that's even allowed.

371
00:12:10,120 --> 00:12:12,560
You have compliance risks spread across three different systems

372
00:12:12,560 --> 00:12:13,720
with no way to manage it.

373
00:12:13,720 --> 00:12:15,360
And then there are the costs.

374
00:12:15,360 --> 00:12:17,560
When you don't have centralized visibility,

375
00:12:17,560 --> 00:12:18,600
you cannot measure.

376
00:12:18,600 --> 00:12:21,400
Each team is consuming tokens against their own budget

377
00:12:21,400 --> 00:12:24,240
or a general cloud spend that nobody is tracking anymore.

378
00:12:24,240 --> 00:12:26,440
The support agent might be calling an expensive model

379
00:12:26,440 --> 00:12:27,640
for every single question

380
00:12:27,640 --> 00:12:30,080
when it could use a cheaper one for routine queries

381
00:12:30,080 --> 00:12:32,560
and the finance agent might be running duplicate logic

382
00:12:32,560 --> 00:12:34,640
that the data team already built.

383
00:12:34,640 --> 00:12:36,440
The recruiting agent might be fetching data

384
00:12:36,440 --> 00:12:39,360
from three different sources when one unified source would work

385
00:12:39,360 --> 00:12:40,320
but nobody knows.

386
00:12:40,320 --> 00:12:42,880
Nobody can see, nobody can optimize.

387
00:12:42,880 --> 00:12:45,360
A poorly optimized agent using an expensive model

388
00:12:45,360 --> 00:12:48,960
with inefficient prompts can easily burn $50,000 a month

389
00:12:48,960 --> 00:12:52,120
and you won't find out until the cloud bill shows up.

390
00:12:52,120 --> 00:12:53,280
By the time you see the bill,

391
00:12:53,280 --> 00:12:54,920
the agent has been running for six months

392
00:12:54,920 --> 00:12:57,800
and the team that built it has already moved on to the next project

393
00:12:57,800 --> 00:13:00,200
when you finally asked them to optimize the costs,

394
00:13:00,200 --> 00:13:02,240
they just shrug because they were never given the tools

395
00:13:02,240 --> 00:13:04,160
to measure consumption in the first place.

396
00:13:04,160 --> 00:13:07,200
This is the financial consequence of distributed governance.

397
00:13:07,200 --> 00:13:08,640
You are paying for invisibility

398
00:13:08,640 --> 00:13:10,760
but the real problem is deeper than cost.

399
00:13:10,760 --> 00:13:11,800
It's compliance.

400
00:13:11,800 --> 00:13:16,000
Compliance requires a single, traceable answer to one question

401
00:13:16,000 --> 00:13:18,440
which systems accessed what data and when.

402
00:13:18,440 --> 00:13:20,800
With three independent teams running independent setups,

403
00:13:20,800 --> 00:13:22,920
that question becomes impossible to answer.

404
00:13:22,920 --> 00:13:24,840
The finance team's logging goes to one place

405
00:13:24,840 --> 00:13:26,520
the support teams go somewhere else.

406
00:13:26,520 --> 00:13:27,920
The data team isn't logging at all.

407
00:13:27,920 --> 00:13:31,280
If a regulator asks if an agent accessed a sensitive record,

408
00:13:31,280 --> 00:13:34,480
you don't have a way to search across all three systems at once

409
00:13:34,480 --> 00:13:36,640
and you don't have a standard for what access even means

410
00:13:36,640 --> 00:13:37,920
or how it gets recorded.

411
00:13:37,920 --> 00:13:39,440
You cannot pass an audit with that.

412
00:13:39,440 --> 00:13:41,360
This is what people mean by governance debt.

413
00:13:41,360 --> 00:13:43,800
You are borrowing against future control.

414
00:13:43,800 --> 00:13:45,560
You are accepting the chaos now in exchange

415
00:13:45,560 --> 00:13:47,080
for the ability to ship fast.

416
00:13:47,080 --> 00:13:49,320
You're trading short term speed for the long term certainty

417
00:13:49,320 --> 00:13:51,080
that you won't be able to govern what you've built.

418
00:13:51,080 --> 00:13:54,080
An unlike financial debt, governance debt compounds.

419
00:13:54,080 --> 00:13:56,080
Every new team that stands up their own system

420
00:13:56,080 --> 00:13:57,400
makes the problem worse.

421
00:13:57,400 --> 00:13:59,400
And every month, you delay centralizing governance

422
00:13:59,400 --> 00:14:01,880
makes the eventual cleanup work much larger.

423
00:14:01,880 --> 00:14:04,000
Eventually, you hit a wall, a compliance failure,

424
00:14:04,000 --> 00:14:07,520
a security incident, a cost overrun, you cannot explain.

425
00:14:07,520 --> 00:14:09,000
And at that point, you have to go back

426
00:14:09,000 --> 00:14:11,120
and rebuild everything you should have built from the start.

427
00:14:11,120 --> 00:14:13,160
The way out starts with a single question,

428
00:14:13,160 --> 00:14:14,760
what if governance happened first?

429
00:14:14,760 --> 00:14:17,720
What a landing zone actually is.

430
00:14:17,720 --> 00:14:19,760
To understand the solution, we need to reframe

431
00:14:19,760 --> 00:14:21,440
what a landing zone actually means.

432
00:14:21,440 --> 00:14:25,080
Most people hear the term and think infrastructure, networking,

433
00:14:25,080 --> 00:14:26,960
cloud subscriptions, the plumbing.

434
00:14:26,960 --> 00:14:29,400
And yes, a landing zone includes those things.

435
00:14:29,400 --> 00:14:31,160
But if that's what you think a landing zone is.

436
00:14:31,160 --> 00:14:32,600
You're looking at it backwards.

437
00:14:32,600 --> 00:14:34,640
A landing zone isn't primarily about networks.

438
00:14:34,640 --> 00:14:37,320
It's about how AI operates in your organization.

439
00:14:37,320 --> 00:14:39,400
It's the operational model that prevents the problems

440
00:14:39,400 --> 00:14:40,480
we just talked about.

441
00:14:40,480 --> 00:14:42,480
The identity collapse, the reasoning gaps,

442
00:14:42,480 --> 00:14:43,680
and the governance debt.

443
00:14:43,680 --> 00:14:45,320
It is the framework that says,

444
00:14:45,320 --> 00:14:46,680
this is how we build agents here.

445
00:14:46,680 --> 00:14:47,880
This is who controls access.

446
00:14:47,880 --> 00:14:48,920
This is what gets logged.

447
00:14:48,920 --> 00:14:51,840
A landing zone is the moment your organization stops treating

448
00:14:51,840 --> 00:14:54,160
agents as one of projects and starts treating them

449
00:14:54,160 --> 00:14:55,640
as a managed capability.

450
00:14:55,640 --> 00:14:58,480
It is the shift from teams building their own fragmented

451
00:14:58,480 --> 00:15:01,400
infrastructure to the organization having a standard way agents

452
00:15:01,400 --> 00:15:03,360
get built, deployed, and retired.

453
00:15:03,360 --> 00:15:06,760
That shift matters because standardization is how you scale safely.

454
00:15:06,760 --> 00:15:08,000
That standard has three layers.

455
00:15:08,000 --> 00:15:09,480
The first is the identity plane.

456
00:15:09,480 --> 00:15:11,720
This is where you solve the accountability problem.

457
00:15:11,720 --> 00:15:13,760
Every agent that runs in your organization

458
00:15:13,760 --> 00:15:16,840
gets a traceable identity, which is an agent ID registered

459
00:15:16,840 --> 00:15:18,880
in your directory just like a user account,

460
00:15:18,880 --> 00:15:20,880
but built for autonomous systems.

461
00:15:20,880 --> 00:15:22,440
That identity carries permissions

462
00:15:22,440 --> 00:15:24,800
scoped to exactly what the agent needs,

463
00:15:24,800 --> 00:15:26,800
including which data it can access,

464
00:15:26,800 --> 00:15:29,520
and which specific models it is allowed to use.

465
00:15:29,520 --> 00:15:30,960
When that agent acts,

466
00:15:30,960 --> 00:15:32,840
the action is attributed to the agent itself

467
00:15:32,840 --> 00:15:34,880
rather than being borrowed from a human account,

468
00:15:34,880 --> 00:15:36,760
so the audit trail stays clean.

469
00:15:36,760 --> 00:15:38,720
The second layer is the governance hub.

470
00:15:38,720 --> 00:15:40,000
This is centralization.

471
00:15:40,000 --> 00:15:42,080
It is the single place where all approved models live,

472
00:15:42,080 --> 00:15:44,760
all tools are catalogued and all approvals flow through.

473
00:15:44,760 --> 00:15:46,000
When a team wants to build an agent,

474
00:15:46,000 --> 00:15:48,840
they don't go configure Azure OpenAI directly.

475
00:15:48,840 --> 00:15:50,360
They request access through the hub.

476
00:15:50,360 --> 00:15:53,080
When they want to add a tool, they don't integrate it independently.

477
00:15:53,080 --> 00:15:55,200
They submitted to the catalog where it is reviewed

478
00:15:55,200 --> 00:15:56,600
and approved for everyone.

479
00:15:56,600 --> 00:15:59,920
If policies change and you need stricter data, residency rules,

480
00:15:59,920 --> 00:16:01,880
you update the policy once in the hub,

481
00:16:01,880 --> 00:16:03,120
and it applies everywhere.

482
00:16:03,120 --> 00:16:05,000
One place, one source of truth.

483
00:16:05,000 --> 00:16:07,080
Without the hub, you have distributed control

484
00:16:07,080 --> 00:16:08,160
that looks like governance,

485
00:16:08,160 --> 00:16:10,240
but is really just decentralized chaos.

486
00:16:10,240 --> 00:16:12,080
With the hub, you have the control plane

487
00:16:12,080 --> 00:16:15,600
that the support team, finance team, and data team all operate under.

488
00:16:15,600 --> 00:16:17,880
They aren't competing for access to different models

489
00:16:17,880 --> 00:16:19,320
or defining their own rules.

490
00:16:19,320 --> 00:16:21,240
They are all operating within the same framework

491
00:16:21,240 --> 00:16:22,560
with the same visibility.

492
00:16:22,560 --> 00:16:24,480
The third layer is the data plane.

493
00:16:24,480 --> 00:16:26,040
This is where ontologies live.

494
00:16:26,040 --> 00:16:27,880
It is the structured model of your business,

495
00:16:27,880 --> 00:16:29,440
including the entities and the rules

496
00:16:29,440 --> 00:16:31,480
that govern how those entities connect.

497
00:16:31,480 --> 00:16:33,400
It isn't stored in three different places

498
00:16:33,400 --> 00:16:34,560
by three different teams.

499
00:16:34,560 --> 00:16:36,160
It is centralized and versioned.

500
00:16:36,160 --> 00:16:37,400
When a new agent gets built,

501
00:16:37,400 --> 00:16:39,360
it doesn't have to figure out what a customer is

502
00:16:39,360 --> 00:16:40,880
or how customers relate to orders

503
00:16:40,880 --> 00:16:42,840
because those definitions already exist.

504
00:16:42,840 --> 00:16:44,480
The agent inherits that understanding

505
00:16:44,480 --> 00:16:46,080
it reasons over the ontology

506
00:16:46,080 --> 00:16:48,920
instead of trying to make sense of disconnected data sources.

507
00:16:48,920 --> 00:16:50,880
That is how you scale consistency

508
00:16:50,880 --> 00:16:52,360
across dozens of agents

509
00:16:52,360 --> 00:16:54,880
without them constantly contradicting each other.

510
00:16:54,880 --> 00:16:56,520
These three layers work together.

511
00:16:56,520 --> 00:16:58,240
Identity makes agents accountable.

512
00:16:58,240 --> 00:17:00,520
The governance hub makes decisions centralized.

513
00:17:00,520 --> 00:17:02,520
The ontology makes reasoning consistent.

514
00:17:02,520 --> 00:17:04,920
Individually, each one solves a structural problem.

515
00:17:04,920 --> 00:17:06,680
But together, they form the architecture

516
00:17:06,680 --> 00:17:08,520
that lets you deploy hundreds of agents

517
00:17:08,520 --> 00:17:09,960
without creating chaos.

518
00:17:09,960 --> 00:17:11,120
And this is the key shift.

519
00:17:11,120 --> 00:17:12,720
You aren't building infrastructure

520
00:17:12,720 --> 00:17:14,800
to support isolated chatbots anymore.

521
00:17:14,800 --> 00:17:17,280
You're building a platform and AI operating system.

522
00:17:17,280 --> 00:17:19,680
It's a foundation that any team can build on

523
00:17:19,680 --> 00:17:21,400
without having to recreate governance

524
00:17:21,400 --> 00:17:23,320
or identity every single time.

525
00:17:23,320 --> 00:17:24,840
That is what a landing zone actually is.

526
00:17:24,840 --> 00:17:26,440
It isn't just plumbing or a checklist.

527
00:17:26,440 --> 00:17:28,080
It is the moment your organization moves

528
00:17:28,080 --> 00:17:29,600
from experimenting with AI

529
00:17:29,600 --> 00:17:32,280
to operating AI responsibly at scale.

530
00:17:32,280 --> 00:17:33,800
The model gateway pattern,

531
00:17:33,800 --> 00:17:36,600
the identity plane and governance hub are foundational

532
00:17:36,600 --> 00:17:38,920
but they only work if you actually control

533
00:17:38,920 --> 00:17:42,160
how agents access models and most organizations don't.

534
00:17:42,160 --> 00:17:43,560
What typically happens is this.

535
00:17:43,560 --> 00:17:46,360
An agent needs to call Azure OpenAI or Anthropic.

536
00:17:46,360 --> 00:17:48,080
Someone generates an API key.

537
00:17:48,080 --> 00:17:49,480
They embed it in the agent's code.

538
00:17:49,480 --> 00:17:51,160
The agent calls the model directly.

539
00:17:51,160 --> 00:17:52,400
No intermediary, no filtering,

540
00:17:52,400 --> 00:17:54,480
no policy check, just agent to model.

541
00:17:54,480 --> 00:17:55,520
This feels efficient.

542
00:17:55,520 --> 00:17:57,640
The agent gets what it needs without extra layers.

543
00:17:57,640 --> 00:18:00,120
There's no latency penalty from rooting through a gateway.

544
00:18:00,120 --> 00:18:01,640
There's no extra service to operate.

545
00:18:01,640 --> 00:18:02,760
The team ships fast.

546
00:18:02,760 --> 00:18:03,880
But you've lost control.

547
00:18:03,880 --> 00:18:07,560
When every agent connects directly to models,

548
00:18:07,560 --> 00:18:09,800
you have no central way to enforce policy.

549
00:18:09,800 --> 00:18:11,760
One agent might be calling an expensive model

550
00:18:11,760 --> 00:18:13,640
when a cheaper one would work.

551
00:18:13,640 --> 00:18:15,520
Another might be sending data to a region

552
00:18:15,520 --> 00:18:17,560
that violates your compliance requirements.

553
00:18:17,560 --> 00:18:19,920
A third might be consuming tokens at a rate

554
00:18:19,920 --> 00:18:22,280
that nobody noticed until the bill arrived.

555
00:18:22,280 --> 00:18:24,080
Each agent is making independent decisions

556
00:18:24,080 --> 00:18:26,360
about which model to use and what data to expose.

557
00:18:26,360 --> 00:18:27,400
There's no enforcement.

558
00:18:27,400 --> 00:18:28,400
There's no visibility.

559
00:18:28,400 --> 00:18:29,640
There's no way to say no.

560
00:18:29,640 --> 00:18:31,160
This is where a model gateway comes in.

561
00:18:31,160 --> 00:18:34,160
A gateway is a single entry point for all AI traffic.

562
00:18:34,160 --> 00:18:35,720
Every agent that needs to call a model

563
00:18:35,720 --> 00:18:37,080
doesn't call it directly.

564
00:18:37,080 --> 00:18:38,440
The agent calls the gateway.

565
00:18:38,440 --> 00:18:40,320
The gateway then decides which model to root

566
00:18:40,320 --> 00:18:42,120
the request to based on policy.

567
00:18:42,120 --> 00:18:44,600
Think of it like a border checkpoint for AI traffic.

568
00:18:44,600 --> 00:18:45,960
What does the gateway do?

569
00:18:45,960 --> 00:18:47,920
At minimum, it handles authentication.

570
00:18:47,920 --> 00:18:50,080
Every request comes with credentials identifying

571
00:18:50,080 --> 00:18:51,920
which agent is making the request.

572
00:18:51,920 --> 00:18:53,920
The gateway validates those credentials

573
00:18:53,920 --> 00:18:55,360
against the agent's permissions.

574
00:18:55,360 --> 00:18:57,920
This is the identity layer you built in the landing zone,

575
00:18:57,920 --> 00:18:59,360
now enforced at runtime.

576
00:18:59,360 --> 00:19:00,760
When agent can't call a model,

577
00:19:00,760 --> 00:19:02,760
it doesn't have permission to use.

578
00:19:02,760 --> 00:19:04,680
But authentication is just the entry point.

579
00:19:04,680 --> 00:19:05,880
Once you have that checkpoint,

580
00:19:05,880 --> 00:19:08,040
you can add policy enforcement on top.

581
00:19:08,040 --> 00:19:09,520
Here's where it gets powerful.

582
00:19:09,520 --> 00:19:11,880
Let's say your organization has a policy.

583
00:19:11,880 --> 00:19:14,640
No customer data leaves the US region.

584
00:19:14,640 --> 00:19:17,480
An agent is built to process customer support tickets.

585
00:19:17,480 --> 00:19:19,880
It's configured to use a model that runs in Europe.

586
00:19:19,880 --> 00:19:22,560
Without a gateway, that agent calls the European model

587
00:19:22,560 --> 00:19:25,640
directly and sends customer data across the ocean.

588
00:19:25,640 --> 00:19:28,040
You find out six months later when an auditor asks

589
00:19:28,040 --> 00:19:29,560
where your customer data goes

590
00:19:29,560 --> 00:19:32,080
where the gateway that request hits the checkpoint.

591
00:19:32,080 --> 00:19:33,920
The gateway evaluates the policy.

592
00:19:33,920 --> 00:19:36,200
This agent is asking to call a European model.

593
00:19:36,200 --> 00:19:38,680
This policy says customer data can't leave the US.

594
00:19:38,680 --> 00:19:39,840
The gateway blocks the request

595
00:19:39,840 --> 00:19:42,240
and re-route it to a US-based model instead.

596
00:19:42,240 --> 00:19:44,120
Policy enforced, no data violation,

597
00:19:44,120 --> 00:19:45,440
no surprise audit failure.

598
00:19:45,440 --> 00:19:48,080
The gateway also handles cost attribution.

599
00:19:48,080 --> 00:19:50,360
Every model call goes through a single point.

600
00:19:50,360 --> 00:19:52,440
You can track which agent called which model,

601
00:19:52,440 --> 00:19:55,360
how many tokens it consumed, and what the cost was.

602
00:19:55,360 --> 00:19:57,000
Instead of discovering in your cloud bill

603
00:19:57,000 --> 00:19:59,560
that you're spending $50,000 a month on model calls,

604
00:19:59,560 --> 00:20:00,880
you have real-time visibility.

605
00:20:00,880 --> 00:20:02,560
You can see that one agent is responsible

606
00:20:02,560 --> 00:20:04,080
for 90% of the spend.

607
00:20:04,080 --> 00:20:04,920
You can optimize it.

608
00:20:04,920 --> 00:20:06,560
You can set budgets and alerts.

609
00:20:06,560 --> 00:20:08,720
You can route expensive queries to cheaper models

610
00:20:08,720 --> 00:20:11,360
or cache results to reduce redundant calls.

611
00:20:11,360 --> 00:20:13,440
Without a gateway, that visibility doesn't exist.

612
00:20:13,440 --> 00:20:16,040
You have 10 agents calling 10 different models

613
00:20:16,040 --> 00:20:17,960
with no way to aggregate the picture.

614
00:20:17,960 --> 00:20:19,560
The gateway also enables logging.

615
00:20:19,560 --> 00:20:22,520
Every call gets recorded, which agent called the model when?

616
00:20:22,520 --> 00:20:23,360
What was the input?

617
00:20:23,360 --> 00:20:24,280
What was the output?

618
00:20:24,280 --> 00:20:26,200
This creates an audit trail for compliance.

619
00:20:26,200 --> 00:20:28,040
It creates evidence if something goes wrong.

620
00:20:28,040 --> 00:20:29,640
It creates the visibility you need

621
00:20:29,640 --> 00:20:31,800
to understand what your agents are actually doing.

622
00:20:31,800 --> 00:20:33,320
And here's the scaling inside.

623
00:20:33,320 --> 00:20:35,400
With a gateway, you govern it the gateway,

624
00:20:35,400 --> 00:20:36,840
not at the application level.

625
00:20:36,840 --> 00:20:38,480
You don't have to reconfigure every agent

626
00:20:38,480 --> 00:20:39,800
every time policy changes.

627
00:20:39,800 --> 00:20:42,280
You update the policy once in one place.

628
00:20:42,280 --> 00:20:43,800
The next request hits the gateway

629
00:20:43,800 --> 00:20:47,160
and the new policy applies, one change, everywhere.

630
00:20:47,160 --> 00:20:48,600
This is why the gateway is essential.

631
00:20:48,600 --> 00:20:50,720
It's the enforcement layer that makes your governance hub

632
00:20:50,720 --> 00:20:52,760
actually work, the ontology layer.

633
00:20:52,760 --> 00:20:54,480
The gateway solves the control problem,

634
00:20:54,480 --> 00:20:55,920
but there's still one layer missing.

635
00:20:55,920 --> 00:20:58,080
Control over what agents access isn't the same

636
00:20:58,080 --> 00:20:59,920
as control over what agents understand.

637
00:20:59,920 --> 00:21:01,520
Let's go back to the data itself.

638
00:21:01,520 --> 00:21:03,680
Most organizations store their operational knowledge

639
00:21:03,680 --> 00:21:04,600
in two places.

640
00:21:04,600 --> 00:21:06,960
They have a data warehouse, tables and columns,

641
00:21:06,960 --> 00:21:09,280
schemas and joins, the relational model

642
00:21:09,280 --> 00:21:12,440
that's been the backbone of business computing for 40 years.

643
00:21:12,440 --> 00:21:15,600
And they have documents, PDFs, wikis, emails, contracts,

644
00:21:15,600 --> 00:21:17,560
the unstructured stuff that people write.

645
00:21:17,560 --> 00:21:19,760
When an agent needs to answer a business question,

646
00:21:19,760 --> 00:21:22,120
it usually has access to one or the other.

647
00:21:22,120 --> 00:21:24,280
Either it's running a query against your warehouse

648
00:21:24,280 --> 00:21:25,720
or it's searching documents

649
00:21:25,720 --> 00:21:27,560
or it's doing both independently

650
00:21:27,560 --> 00:21:29,480
and trying to reconcile the results.

651
00:21:29,480 --> 00:21:31,240
But neither of those is a business model.

652
00:21:31,240 --> 00:21:33,480
Neither one captures what your business actually is.

653
00:21:33,480 --> 00:21:34,840
Your business isn't a schema.

654
00:21:34,840 --> 00:21:36,760
Your business is a set of relationships.

655
00:21:36,760 --> 00:21:37,720
You have customers.

656
00:21:37,720 --> 00:21:40,480
Those customers place orders, orders contain products.

657
00:21:40,480 --> 00:21:41,920
Products come from suppliers.

658
00:21:41,920 --> 00:21:43,360
Suppliers have lead times.

659
00:21:43,360 --> 00:21:45,040
Customers have credit limits.

660
00:21:45,040 --> 00:21:48,320
Orders that exceed a customer's credit limit need approval.

661
00:21:48,320 --> 00:21:50,320
Some suppliers are marked as high risk,

662
00:21:50,320 --> 00:21:52,400
which affects how their orders are handled.

663
00:21:52,400 --> 00:21:53,880
Orders that go to certain regions

664
00:21:53,880 --> 00:21:55,880
trigger different compliance rules.

665
00:21:55,880 --> 00:21:59,280
A delayed shipment to a VIP customer is a priority incident.

666
00:21:59,280 --> 00:22:02,120
A routine delay to a standard customer is normal.

667
00:22:02,120 --> 00:22:03,400
None of that lives in a table.

668
00:22:03,400 --> 00:22:04,640
None of that is a document.

669
00:22:04,640 --> 00:22:05,880
All of it is business logic.

670
00:22:05,880 --> 00:22:07,040
All of it is meaning.

671
00:22:07,040 --> 00:22:08,960
An ontology is how you encode that meaning.

672
00:22:08,960 --> 00:22:10,680
Think of it as a map of your business.

673
00:22:10,680 --> 00:22:13,480
Not a map of your data, a map of your actual business.

674
00:22:13,480 --> 00:22:14,280
Entities.

675
00:22:14,280 --> 00:22:14,920
Customer.

676
00:22:14,920 --> 00:22:15,520
Order.

677
00:22:15,520 --> 00:22:16,160
Product.

678
00:22:16,160 --> 00:22:17,040
Supplier.

679
00:22:17,040 --> 00:22:17,680
Region.

680
00:22:17,680 --> 00:22:18,480
Acid.

681
00:22:18,480 --> 00:22:20,120
Each one exists as a concept.

682
00:22:20,120 --> 00:22:21,240
Each one has properties.

683
00:22:21,240 --> 00:22:24,120
A customer has a name, an account number, a credit limit,

684
00:22:24,120 --> 00:22:25,360
and a payment history.

685
00:22:25,360 --> 00:22:26,920
An order has an order number, a date,

686
00:22:26,920 --> 00:22:28,680
a customer, products, and a status.

687
00:22:28,680 --> 00:22:30,400
But here's what makes an ontology different

688
00:22:30,400 --> 00:22:31,400
from a spreadsheet.

689
00:22:31,400 --> 00:22:32,760
Relationships matter.

690
00:22:32,760 --> 00:22:35,480
A customer places orders, an order contains products.

691
00:22:35,480 --> 00:22:36,960
A product comes from a supplier.

692
00:22:36,960 --> 00:22:40,040
A supplier operates in regions and asks it belongs to a location.

693
00:22:40,040 --> 00:22:41,240
An order goes to a location.

694
00:22:41,240 --> 00:22:42,440
A location has inventory.

695
00:22:42,440 --> 00:22:44,440
Inventory of a product comes from a supplier.

696
00:22:44,440 --> 00:22:46,720
When you map these relationships explicitly,

697
00:22:46,720 --> 00:22:47,760
something changes.

698
00:22:47,760 --> 00:22:50,280
An agent doesn't have to guess at meaning anymore.

699
00:22:50,280 --> 00:22:52,680
It doesn't have to infer that a customer is high value

700
00:22:52,680 --> 00:22:54,680
by looking at aggregate spending numbers.

701
00:22:54,680 --> 00:22:56,160
The ontology can tell it directly.

702
00:22:56,160 --> 00:22:58,880
The ontology can say this customer has a VIP flag.

703
00:22:58,880 --> 00:23:01,920
When a VIP customer's order is delayed, this matters more.

704
00:23:01,920 --> 00:23:03,720
The agent reasons across the relationships

705
00:23:03,720 --> 00:23:05,360
instead of guessing at patterns.

706
00:23:05,360 --> 00:23:08,200
This matters because business logic lives in relationships.

707
00:23:08,200 --> 00:23:09,800
A standard query of your warehouse

708
00:23:09,800 --> 00:23:12,000
might tell you that a customer hasn't paid their invoice

709
00:23:12,000 --> 00:23:13,240
in 60 days.

710
00:23:13,240 --> 00:23:16,440
An ontology can tell you not just that, but the context.

711
00:23:16,440 --> 00:23:17,920
What region are they in?

712
00:23:17,920 --> 00:23:19,640
What's the payment term for that region?

713
00:23:19,640 --> 00:23:21,360
Are they a long-standing customer?

714
00:23:21,360 --> 00:23:22,720
With a history of late payment

715
00:23:22,720 --> 00:23:25,240
and what was their last interaction with support?

716
00:23:25,240 --> 00:23:27,200
All of that is implicit in the relationships.

717
00:23:27,200 --> 00:23:30,520
The agent doesn't have to construct it from disconnected facts.

718
00:23:30,520 --> 00:23:32,160
And when the agent needs to make a decision,

719
00:23:32,160 --> 00:23:34,200
the ontology acts as a constrained layer.

720
00:23:34,200 --> 00:23:37,040
In a normal data warehouse, an agent can pull whatever it wants

721
00:23:37,040 --> 00:23:38,680
and do whatever it wants with it.

722
00:23:38,680 --> 00:23:41,760
In an ontology, the agent understands what paths are valid.

723
00:23:41,760 --> 00:23:44,240
A marketing agent can see customer spend patterns.

724
00:23:44,240 --> 00:23:46,120
A security agent can see access logs,

725
00:23:46,120 --> 00:23:47,840
but neither one should be able to see data

726
00:23:47,840 --> 00:23:49,160
that's outside their scope.

727
00:23:49,160 --> 00:23:51,120
The ontology enforces that implicitly

728
00:23:51,120 --> 00:23:52,880
through the relationships it defines.

729
00:23:52,880 --> 00:23:54,680
If a path doesn't exist in the ontology,

730
00:23:54,680 --> 00:23:56,160
the agent can't traverse it.

731
00:23:56,160 --> 00:23:59,440
This is how hallucination gets prevented at a structural level.

732
00:23:59,440 --> 00:24:01,760
When an agent is reasoning over an ontology,

733
00:24:01,760 --> 00:24:03,480
it's not pattern matching against documents

734
00:24:03,480 --> 00:24:05,320
or guessing a table relationships.

735
00:24:05,320 --> 00:24:08,040
It's traversing an explicit model of your business.

736
00:24:08,040 --> 00:24:10,560
Every relationship has been intentionally defined.

737
00:24:10,560 --> 00:24:12,280
Every entity has been carefully modeled.

738
00:24:12,280 --> 00:24:13,880
The agent doesn't have creative freedom

739
00:24:13,880 --> 00:24:16,160
to interpret data in ways the business doesn't intend.

740
00:24:16,160 --> 00:24:17,520
It operates within the constraints

741
00:24:17,520 --> 00:24:19,160
of how you've defined your business.

742
00:24:19,160 --> 00:24:20,600
And here's the scaling insight.

743
00:24:20,600 --> 00:24:22,760
When you add a new agent to your organization,

744
00:24:22,760 --> 00:24:24,600
it doesn't have to rebuild the ontology.

745
00:24:24,600 --> 00:24:25,560
It inherits it.

746
00:24:25,560 --> 00:24:27,840
The same customer entity, the same VIP flag

747
00:24:27,840 --> 00:24:29,720
and the same relationships between order

748
00:24:29,720 --> 00:24:30,960
and product and supplier.

749
00:24:30,960 --> 00:24:32,960
10 agents, 50 agents, 100 agents.

750
00:24:32,960 --> 00:24:35,160
They all reason over the same business model.

751
00:24:35,160 --> 00:24:37,640
They all make decisions using the same definitions.

752
00:24:37,640 --> 00:24:39,680
Consistency is structural, not achieved

753
00:24:39,680 --> 00:24:41,400
through training or documentation

754
00:24:41,400 --> 00:24:44,040
or hoping everyone remembers how things work.

755
00:24:44,040 --> 00:24:48,000
The shift from chat to fabric, identity, reasoning, governance,

756
00:24:48,000 --> 00:24:50,480
the gateway and the ontology.

757
00:24:50,480 --> 00:24:52,920
Up until now, we've looked at these as individual layers,

758
00:24:52,920 --> 00:24:54,880
but these pieces only matter when they combine,

759
00:24:54,880 --> 00:24:57,280
when they merge, they change the fundamental nature

760
00:24:57,280 --> 00:24:58,800
of what an agent actually is.

761
00:24:58,800 --> 00:25:00,480
That shift is from chat to fabric.

762
00:25:00,480 --> 00:25:02,200
Most people think chat is the full picture.

763
00:25:02,200 --> 00:25:03,280
You ask a question.

764
00:25:03,280 --> 00:25:05,280
The agent processes it, you get an answer,

765
00:25:05,280 --> 00:25:07,680
then you repeat the process, but in reality,

766
00:25:07,680 --> 00:25:09,800
each of those conversations is isolated.

767
00:25:09,800 --> 00:25:12,480
The agent has no memory of what you asked two minutes ago,

768
00:25:12,480 --> 00:25:13,880
no context carries forward.

769
00:25:13,880 --> 00:25:15,600
Every prompt starts fresh.

770
00:25:15,600 --> 00:25:18,400
The agent is stateless, which means it has no understanding

771
00:25:18,400 --> 00:25:19,760
of your broader business landscape.

772
00:25:19,760 --> 00:25:20,960
It doesn't know your history.

773
00:25:20,960 --> 00:25:22,920
It doesn't see the relationship between a decision made

774
00:25:22,920 --> 00:25:24,960
yesterday and a decision being made today.

775
00:25:24,960 --> 00:25:26,720
This works fine for simple scenarios.

776
00:25:26,720 --> 00:25:27,600
What's the weather?

777
00:25:27,600 --> 00:25:29,440
What's the office policy on remote work?

778
00:25:29,440 --> 00:25:30,600
What are the products specs?

779
00:25:30,600 --> 00:25:32,240
One question, one answer done.

780
00:25:32,240 --> 00:25:33,720
But business doesn't work that way.

781
00:25:33,720 --> 00:25:35,400
Real business is about understanding

782
00:25:35,400 --> 00:25:36,640
chains of cause and effect.

783
00:25:36,640 --> 00:25:37,920
Why did revenue drop?

784
00:25:37,920 --> 00:25:40,720
Because a major customer didn't place their monthly order.

785
00:25:40,720 --> 00:25:42,160
Why didn't they place that order?

786
00:25:42,160 --> 00:25:43,600
Because their inventory is full.

787
00:25:43,600 --> 00:25:44,720
Why is their inventory full?

788
00:25:44,720 --> 00:25:47,600
Because a supplier delay backed up their entire supply chain.

789
00:25:47,600 --> 00:25:48,960
Why was there a delay?

790
00:25:48,960 --> 00:25:52,200
Because that supplier operates in a region hit by new tariffs.

791
00:25:52,200 --> 00:25:53,760
How does that affect our forecasting?

792
00:25:53,760 --> 00:25:55,920
What do we tell Wall Street about Q3?

793
00:25:55,920 --> 00:25:57,800
Each of those questions requires understanding

794
00:25:57,800 --> 00:25:58,680
the previous answer.

795
00:25:58,680 --> 00:26:00,720
They require context that accumulates over time.

796
00:26:00,720 --> 00:26:01,800
A chat agent can't do that.

797
00:26:01,800 --> 00:26:05,040
It can answer what is our revenue in total isolation.

798
00:26:05,040 --> 00:26:06,960
But it can't answer why did it drop?

799
00:26:06,960 --> 00:26:08,360
And what should we do about it?

800
00:26:08,360 --> 00:26:11,480
Answering that requires holding multiple pieces of context

801
00:26:11,480 --> 00:26:13,080
in relationship to each other.

802
00:26:13,080 --> 00:26:14,240
All at once.

803
00:26:14,240 --> 00:26:16,280
This is the difference between stateless and stateful.

804
00:26:16,280 --> 00:26:17,680
Chat is stateless.

805
00:26:17,680 --> 00:26:19,840
Fabric is stateful.

806
00:26:19,840 --> 00:26:21,680
With Fabric, the agent understands

807
00:26:21,680 --> 00:26:23,840
the business landscape surrounding the question.

808
00:26:23,840 --> 00:26:26,240
It knows this specific customer is high value.

809
00:26:26,240 --> 00:26:28,120
It knows their inventory patterns predict

810
00:26:28,120 --> 00:26:29,480
when they will order next.

811
00:26:29,480 --> 00:26:32,200
It knows the supplier risk in their region has changed.

812
00:26:32,200 --> 00:26:34,960
It understands the full chain that led to the revenue drop.

813
00:26:34,960 --> 00:26:37,040
It isn't answering questions in a vacuum.

814
00:26:37,040 --> 00:26:40,040
It is reasoning across a continuous model of your business.

815
00:26:40,040 --> 00:26:41,800
The operational consequence is profound.

816
00:26:41,800 --> 00:26:43,360
With chat agents are reactive.

817
00:26:43,360 --> 00:26:45,200
You point them at a question and they answer it.

818
00:26:45,200 --> 00:26:47,600
They are useful for someone looking for a quick response.

819
00:26:47,600 --> 00:26:48,880
But they can't be proactive.

820
00:26:48,880 --> 00:26:51,800
They can't detect patterns before those patterns become problems.

821
00:26:51,800 --> 00:26:54,160
They can't understand causation across multiple systems.

822
00:26:54,160 --> 00:26:57,000
They can't make the connection between a supplier delay in Asia

823
00:26:57,000 --> 00:26:59,120
and a cash flow problem in North America.

824
00:26:59,120 --> 00:27:01,120
With Fabric, agents become orchestrators.

825
00:27:01,120 --> 00:27:02,440
They see the full picture.

826
00:27:02,440 --> 00:27:04,520
They can monitor multiple signals simultaneously.

827
00:27:04,520 --> 00:27:06,280
They can detect when something is about to go wrong

828
00:27:06,280 --> 00:27:07,760
before it actually happens.

829
00:27:07,760 --> 00:27:10,360
They can recommend actions based on the complete chain.

830
00:27:10,360 --> 00:27:11,800
They aren't just responding to questions.

831
00:27:11,800 --> 00:27:14,440
They are actively maintaining the health of the system.

832
00:27:14,440 --> 00:27:17,720
Consider what happens when a major customer hasn't ordered in two months.

833
00:27:17,720 --> 00:27:20,560
A chat agent searches for recent orders, finds none,

834
00:27:20,560 --> 00:27:22,840
and reports no recent orders.

835
00:27:22,840 --> 00:27:23,760
That's the answer.

836
00:27:23,760 --> 00:27:25,080
Information delivered.

837
00:27:25,080 --> 00:27:28,000
A Fabric agent understands this customer's historical pattern.

838
00:27:28,000 --> 00:27:30,240
It detects they are three weeks past their normal cycle.

839
00:27:30,240 --> 00:27:33,160
It checks their inventory levels and correlates that with supplier data

840
00:27:33,160 --> 00:27:34,400
to see if a backup is coming.

841
00:27:34,400 --> 00:27:37,320
It recognizes this customer is a VIP and that delays

842
00:27:37,320 --> 00:27:39,440
now create cascading problems downstream.

843
00:27:39,440 --> 00:27:41,720
Then it escalates this as a priority incident

844
00:27:41,720 --> 00:27:44,160
and recommends outreach with a targeted offer

845
00:27:44,160 --> 00:27:45,720
before the problem becomes critical.

846
00:27:45,720 --> 00:27:49,760
Same agent, different architecture, one saw a fact, one understood a situation.

847
00:27:49,760 --> 00:27:51,640
This is where the infrastructure you've built

848
00:27:51,640 --> 00:27:53,960
finally becomes more than technical scaffolding.

849
00:27:53,960 --> 00:27:56,360
This is where you stop managing a collection of chatbots

850
00:27:56,360 --> 00:27:58,480
and start managing an intelligent system

851
00:27:58,480 --> 00:28:00,760
where identity isn't just compliance theatre,

852
00:28:00,760 --> 00:28:02,640
where the ontology isn't just a database,

853
00:28:02,640 --> 00:28:05,600
where governance isn't just control for the sake of control.

854
00:28:05,600 --> 00:28:07,960
The organization that moves from chat to fabric

855
00:28:07,960 --> 00:28:10,760
is fundamentally changing how intelligence operates.

856
00:28:10,760 --> 00:28:13,480
From isolated questions to coordinated reasoning,

857
00:28:13,480 --> 00:28:15,400
from waiting for problems to detecting them,

858
00:28:15,400 --> 00:28:17,040
from a feature to a platform,

859
00:28:17,040 --> 00:28:19,520
agent 365 as the control plane,

860
00:28:19,520 --> 00:28:21,720
but building identity, reasoning, governance,

861
00:28:21,720 --> 00:28:23,720
and a gateway separately doesn't scale.

862
00:28:23,720 --> 00:28:25,440
These are infrastructure components.

863
00:28:25,440 --> 00:28:26,840
They still need to be orchestrated.

864
00:28:26,840 --> 00:28:29,040
They still need to be operated as a single system

865
00:28:29,040 --> 00:28:31,400
without a unified layer that understands all four.

866
00:28:31,400 --> 00:28:33,240
You are left with plumbing that doesn't connect.

867
00:28:33,240 --> 00:28:36,200
You need a layer that can inventory agents in force identity,

868
00:28:36,200 --> 00:28:39,080
apply rules and surface observability all at once.

869
00:28:39,080 --> 00:28:40,960
This is what agent 365 does.

870
00:28:40,960 --> 00:28:44,480
Agent 365 is Microsoft's answer to a practical problem.

871
00:28:44,480 --> 00:28:46,680
How do you actually operate an agent architecture

872
00:28:46,680 --> 00:28:48,360
across an entire organization?

873
00:28:48,360 --> 00:28:51,400
It is the control plane that ties together everything we've discussed.

874
00:28:51,400 --> 00:28:54,200
It's a single place where you can see every agent that exists.

875
00:28:54,200 --> 00:28:55,720
You can trace every action it takes.

876
00:28:55,720 --> 00:28:57,840
You can enforce every policy you've defined.

877
00:28:57,840 --> 00:28:59,560
And you can manage its complete life cycle,

878
00:28:59,560 --> 00:29:01,800
think of it as the operating system for agents.

879
00:29:01,800 --> 00:29:04,040
Without it, agents scatter.

880
00:29:04,040 --> 00:29:06,560
A team in support builds an agent in co-pilot studio.

881
00:29:06,560 --> 00:29:09,120
A team in finance builds one in Azure AI Foundry.

882
00:29:09,120 --> 00:29:11,880
Data signs deploys a custom framework on Kubernetes,

883
00:29:11,880 --> 00:29:14,160
marketing experiments with an open source tool.

884
00:29:14,160 --> 00:29:17,520
In six months, you have agents running on five different platforms,

885
00:29:17,520 --> 00:29:20,320
built with different tools using different policies.

886
00:29:20,320 --> 00:29:22,920
And logging to different systems, you don't have an architecture,

887
00:29:22,920 --> 00:29:25,360
you have fragments that happen to do similar things.

888
00:29:25,360 --> 00:29:28,160
Agent 365 brings them into one visibility layer.

889
00:29:28,160 --> 00:29:30,040
Every agent gets registered in the control plane

890
00:29:30,040 --> 00:29:32,600
regardless of where it was built or what framework it uses.

891
00:29:32,600 --> 00:29:33,960
That agent gets an identity.

892
00:29:33,960 --> 00:29:36,080
That identity gets permissions.

893
00:29:36,080 --> 00:29:37,520
Those permissions get logged.

894
00:29:37,520 --> 00:29:39,240
The agent's behavior gets monitored.

895
00:29:39,240 --> 00:29:40,920
All of it flows through a single system.

896
00:29:40,920 --> 00:29:42,160
What does that actually accomplish?

897
00:29:42,160 --> 00:29:44,680
First, registry.

898
00:29:44,680 --> 00:29:46,200
You can answer the basic question.

899
00:29:46,200 --> 00:29:48,360
What agents exist in our organization?

900
00:29:48,360 --> 00:29:49,480
Not what we think exists.

901
00:29:49,480 --> 00:29:50,800
What actually exists?

902
00:29:50,800 --> 00:29:53,800
Agent 365 provides formal registration and discovery.

903
00:29:53,800 --> 00:29:55,720
It can scan your environment and find agents

904
00:29:55,720 --> 00:29:57,600
that are running without being registered.

905
00:29:57,600 --> 00:29:59,640
These are the agents that exist in the shadows

906
00:29:59,640 --> 00:30:02,800
because they were built quickly and never went through onboarding.

907
00:30:02,800 --> 00:30:04,640
Second, identity management.

908
00:30:04,640 --> 00:30:06,880
Every agent that registers gets an agent ID,

909
00:30:06,880 --> 00:30:09,200
this is a proper directory identity in Entra

910
00:30:09,200 --> 00:30:10,880
that agent can be assigned permissions

911
00:30:10,880 --> 00:30:12,720
scoped to what it actually needs.

912
00:30:12,720 --> 00:30:14,280
Those permissions are enforced by the gateway

913
00:30:14,280 --> 00:30:15,840
invalidated by the ontology.

914
00:30:15,840 --> 00:30:17,320
They are logged and auditable.

915
00:30:17,320 --> 00:30:20,120
When that agent acts, the action is attributed to that agent,

916
00:30:20,120 --> 00:30:21,840
not to a human or a shared account.

917
00:30:21,840 --> 00:30:24,080
Accountability becomes possible.

918
00:30:24,080 --> 00:30:25,840
Third, policy enforcement.

919
00:30:25,840 --> 00:30:28,040
Your governance hub defines the rules,

920
00:30:28,040 --> 00:30:30,520
which models can be used, which tools are approved,

921
00:30:30,520 --> 00:30:32,200
which data sources are accessible.

922
00:30:32,200 --> 00:30:34,160
Those policies live in agent 365.

923
00:30:34,160 --> 00:30:35,440
When an agent tries to do something,

924
00:30:35,440 --> 00:30:38,000
agent 365 checks if that action is permitted,

925
00:30:38,000 --> 00:30:40,880
does the policy allow this agent to call this specific model?

926
00:30:40,880 --> 00:30:42,360
Does it allow accessing this data?

927
00:30:42,360 --> 00:30:44,680
Is this operation happening in an approved region?

928
00:30:44,680 --> 00:30:46,520
These questions get answered at runtime.

929
00:30:46,520 --> 00:30:49,080
Policies are enforced consistently across every agent,

930
00:30:49,080 --> 00:30:50,320
no matter how it was built.

931
00:30:50,320 --> 00:30:52,080
Fourth, observability.

932
00:30:52,080 --> 00:30:54,600
Agent 365 doesn't just enforce, it watches.

933
00:30:54,600 --> 00:30:56,560
It logs every action, every API call,

934
00:30:56,560 --> 00:30:57,600
and every data access.

935
00:30:57,600 --> 00:30:59,800
It correlates those logs with the agent's identity

936
00:30:59,800 --> 00:31:01,560
and the policies that were enforced.

937
00:31:01,560 --> 00:31:03,520
When something goes wrong, you can trace it back.

938
00:31:03,520 --> 00:31:05,720
You can understand what the agent did and why it did it.

939
00:31:05,720 --> 00:31:07,200
You can see what data it accessed

940
00:31:07,200 --> 00:31:08,600
and what decisions it made.

941
00:31:08,600 --> 00:31:11,200
That is the audit trail that compliance requires.

942
00:31:11,200 --> 00:31:12,600
And here is the lifecycle benefit

943
00:31:12,600 --> 00:31:15,200
that nobody talks about until it becomes a crisis.

944
00:31:15,200 --> 00:31:17,120
Agents get built, they accomplish their purpose,

945
00:31:17,120 --> 00:31:18,960
the project ends.

946
00:31:18,960 --> 00:31:20,040
But the agent keeps running.

947
00:31:20,040 --> 00:31:22,360
Six months later, the person who built it is gone.

948
00:31:22,360 --> 00:31:25,320
A year later, nobody even remembers what it was supposed to do.

949
00:31:25,320 --> 00:31:26,760
It is still consuming tokens.

950
00:31:26,760 --> 00:31:28,320
It is still accessing data.

951
00:31:28,320 --> 00:31:30,120
It might be configured with permissions

952
00:31:30,120 --> 00:31:31,920
that were reasonable when it was built,

953
00:31:31,920 --> 00:31:33,120
but are now inappropriate.

954
00:31:33,120 --> 00:31:36,320
It's a ghost, it's a cost drain, it's a security risk.

955
00:31:36,320 --> 00:31:39,840
Agent 365 tracks ownership and usage.

956
00:31:39,840 --> 00:31:42,200
You can see which agents haven't been used in six months.

957
00:31:42,200 --> 00:31:44,360
You can see which ones don't have a named owner.

958
00:31:44,360 --> 00:31:46,360
You can see which ones are consuming resources.

959
00:31:46,360 --> 00:31:48,600
Disproportionately, you can make data-driven decisions

960
00:31:48,600 --> 00:31:51,280
about retirement instead of just letting them pile up.

961
00:31:51,280 --> 00:31:53,280
The shift here is subtle, but absolute.

962
00:31:53,280 --> 00:31:55,640
Without Agent 365, you are managing agents

963
00:31:55,640 --> 00:31:58,520
through individual conversations with individual teams.

964
00:31:58,520 --> 00:32:00,160
Have you checked on your agents lately?

965
00:32:00,160 --> 00:32:03,760
With Agent 365, you are managing agents as a portfolio.

966
00:32:03,760 --> 00:32:05,600
You have visibility, you have control,

967
00:32:05,600 --> 00:32:07,520
you have the operational backbone

968
00:32:07,520 --> 00:32:09,320
that lets governance actually work.

969
00:32:09,320 --> 00:32:12,880
Agent stop being shadow projects and they become managed assets.

970
00:32:12,880 --> 00:32:16,480
The governance hub, Agent 365 gives you visibility and identity,

971
00:32:16,480 --> 00:32:19,160
but visibility without enforcement is just information.

972
00:32:19,160 --> 00:32:21,640
You can see what agents are doing and know who is doing it,

973
00:32:21,640 --> 00:32:23,840
but that doesn't stop them from doing the wrong thing.

974
00:32:23,840 --> 00:32:25,800
That's where the governance hub comes in.

975
00:32:25,800 --> 00:32:27,760
If Agent 365 is the control plane,

976
00:32:27,760 --> 00:32:29,800
the governance hub is the policy engine.

977
00:32:29,800 --> 00:32:32,560
It is the central registry for every approved model,

978
00:32:32,560 --> 00:32:35,440
every vetted tool, every authorized data source,

979
00:32:35,440 --> 00:32:37,920
and every rule that governs how they are used.

980
00:32:37,920 --> 00:32:39,040
It isn't distributed.

981
00:32:39,040 --> 00:32:41,120
It isn't something each team maintains locally.

982
00:32:41,120 --> 00:32:43,000
It is one place, one source of truth.

983
00:32:43,000 --> 00:32:44,120
But here is the problem.

984
00:32:44,120 --> 00:32:46,840
Without a hub, developers make independent decisions

985
00:32:46,840 --> 00:32:48,160
about what they integrate.

986
00:32:48,160 --> 00:32:51,120
A team building an agent needs a tool that connects to Salesforce

987
00:32:51,120 --> 00:32:54,000
instead of checking a catalog of approved Salesforce connectors

988
00:32:54,000 --> 00:32:56,360
they build their own because there is no catalog.

989
00:32:56,360 --> 00:32:57,280
They build it fast.

990
00:32:57,280 --> 00:32:57,800
It works.

991
00:32:57,800 --> 00:32:58,520
It chips.

992
00:32:58,520 --> 00:33:00,680
Six months later, another team builds the same connector

993
00:33:00,680 --> 00:33:02,600
because they also didn't know it existed.

994
00:33:02,600 --> 00:33:06,000
Now you have duplicate functionality, inconsistent implementations,

995
00:33:06,000 --> 00:33:08,240
and two different connectors that might behave differently

996
00:33:08,240 --> 00:33:10,200
or have different security properties.

997
00:33:10,200 --> 00:33:12,640
Multiply that by dozens of teams and dozens of tools.

998
00:33:12,640 --> 00:33:14,440
You end up with a sprawl of integrations

999
00:33:14,440 --> 00:33:16,560
that nobody understands and nobody can maintain.

1000
00:33:16,560 --> 00:33:18,040
A governance hub prevents that.

1001
00:33:18,040 --> 00:33:21,080
Every tool that an agent is allowed to use gets registered there.

1002
00:33:21,080 --> 00:33:21,880
It has been reviewed.

1003
00:33:21,880 --> 00:33:22,720
It has been tested.

1004
00:33:22,720 --> 00:33:24,680
Its security properties are documented.

1005
00:33:24,680 --> 00:33:26,560
When a new team wants to integrate Salesforce,

1006
00:33:26,560 --> 00:33:28,040
they don't build a new connector.

1007
00:33:28,040 --> 00:33:29,800
They request access to the approved one.

1008
00:33:29,800 --> 00:33:31,360
The request goes through the hub.

1009
00:33:31,360 --> 00:33:33,240
The hub checks their agents permissions.

1010
00:33:33,240 --> 00:33:35,440
If their agent is authorized to use that tool,

1011
00:33:35,440 --> 00:33:36,280
they get access.

1012
00:33:36,280 --> 00:33:37,600
If not, they are denied.

1013
00:33:37,600 --> 00:33:41,320
But they are also told what other approved tools might solve their problem.

1014
00:33:41,320 --> 00:33:42,720
This shifts the dynamic.

1015
00:33:42,720 --> 00:33:44,440
Instead of every team building independently

1016
00:33:44,440 --> 00:33:45,480
and hoping nothing breaks,

1017
00:33:45,480 --> 00:33:47,800
every team operates within a control set of options.

1018
00:33:47,800 --> 00:33:49,600
Standardization becomes possible.

1019
00:33:49,600 --> 00:33:51,400
The same principle applies to models.

1020
00:33:51,400 --> 00:33:54,720
Without a hub, teams go directly to Azure Open AI or Anthropic

1021
00:33:54,720 --> 00:33:56,320
or whatever they decide to use.

1022
00:33:56,320 --> 00:33:58,560
Some use expensive models for simple tasks.

1023
00:33:58,560 --> 00:34:01,560
Some use models that are restricted to certain regions when they shouldn't be.

1024
00:34:01,560 --> 00:34:03,440
Some use models that have safety properties

1025
00:34:03,440 --> 00:34:04,920
their agents shouldn't violate.

1026
00:34:04,920 --> 00:34:07,400
Without central control, you get inconsistency

1027
00:34:07,400 --> 00:34:09,520
with the hub approved models are listed.

1028
00:34:09,520 --> 00:34:11,680
Teams request the right model for their use case.

1029
00:34:11,680 --> 00:34:14,000
The hub validates that the model is appropriate.

1030
00:34:14,000 --> 00:34:16,400
It validates that the agent has permission to use it.

1031
00:34:16,400 --> 00:34:18,720
It validates that the use case is compliant.

1032
00:34:18,720 --> 00:34:21,080
But here is where the hub becomes operationally essential.

1033
00:34:21,080 --> 00:34:22,280
Policy updates.

1034
00:34:22,280 --> 00:34:26,720
Let's say your compliance team decides that agents can no longer process customer data

1035
00:34:26,720 --> 00:34:27,880
in the European region.

1036
00:34:27,880 --> 00:34:30,840
Every agent needs to use models that run in North America only.

1037
00:34:30,840 --> 00:34:34,000
Without a hub, you email this requirement to all the teams.

1038
00:34:34,000 --> 00:34:35,920
You hope they update their configurations.

1039
00:34:35,920 --> 00:34:37,920
Some do, some don't, some miss the email,

1040
00:34:37,920 --> 00:34:39,640
some interpret the rule differently.

1041
00:34:39,640 --> 00:34:43,240
You end up with agents in compliance and agents that violate the requirement

1042
00:34:43,240 --> 00:34:45,160
and you won't find out until an audit.

1043
00:34:45,160 --> 00:34:49,360
With a hub that policy gets updated once every agent that requests access to a model

1044
00:34:49,360 --> 00:34:51,520
now gets evaluated against that policy.

1045
00:34:51,520 --> 00:34:54,160
An agent tries to call a European model.

1046
00:34:54,160 --> 00:34:56,080
The hub evaluates the request.

1047
00:34:56,080 --> 00:34:58,240
The agent's permissions allow European models,

1048
00:34:58,240 --> 00:35:01,920
but the organization's policy now forbids customer data in that region.

1049
00:35:01,920 --> 00:35:04,440
The hub blocks it and suggests an alternative.

1050
00:35:04,440 --> 00:35:05,640
Policy enforced.

1051
00:35:05,640 --> 00:35:08,840
Everywhere, immediately, no individual configurations,

1052
00:35:08,840 --> 00:35:12,200
no hope that teams remember to update, no surprise audit failures.

1053
00:35:12,200 --> 00:35:13,720
This is operational leverage.

1054
00:35:13,720 --> 00:35:14,960
One policy change.

1055
00:35:14,960 --> 00:35:16,440
Hundreds of agents affected.

1056
00:35:16,440 --> 00:35:17,840
Consistent enforcement.

1057
00:35:17,840 --> 00:35:21,880
The audit benefit is equally significant when a regulator asks which tools your agents

1058
00:35:21,880 --> 00:35:24,240
are using and whether they are approved, you have an answer.

1059
00:35:24,240 --> 00:35:26,480
The governance hub is the authoritative list.

1060
00:35:26,480 --> 00:35:28,440
Every agent using an unapproved tool stands out.

1061
00:35:28,440 --> 00:35:29,120
You can see it.

1062
00:35:29,120 --> 00:35:30,240
You can trace why.

1063
00:35:30,240 --> 00:35:31,080
You can fix it.

1064
00:35:31,080 --> 00:35:34,120
This is what transforms policy from a document that people maybe read into

1065
00:35:34,120 --> 00:35:38,520
infrastructure that actually prevents violations from happening in the first place.

1066
00:35:38,520 --> 00:35:40,080
Data residency and compliance.

1067
00:35:40,080 --> 00:35:43,320
There is a category of problem that most organizations don't think about until

1068
00:35:43,320 --> 00:35:45,320
a regulator asks a question they can't answer.

1069
00:35:45,320 --> 00:35:48,120
It isn't about visibility or governance or even identity.

1070
00:35:48,120 --> 00:35:50,240
It is about something simpler and more fundamental.

1071
00:35:50,240 --> 00:35:51,880
Where your data actually lives.

1072
00:35:51,880 --> 00:35:53,200
Compliance isn't optional.

1073
00:35:53,200 --> 00:35:55,960
It is the cost of operating in regulated industries.

1074
00:35:55,960 --> 00:36:00,280
GDPR says personal data from EU citizens can't be processed outside the EU

1075
00:36:00,280 --> 00:36:01,880
without explicit mechanisms.

1076
00:36:01,880 --> 00:36:04,520
HIPAA says health information can't leave the United States.

1077
00:36:04,520 --> 00:36:09,640
SOC2 says you need to maintain data residency policies and prove you are enforcing them.

1078
00:36:09,640 --> 00:36:13,280
PCI DSS restricts where payment card data can be stored and processed.

1079
00:36:13,280 --> 00:36:14,480
These aren't suggestions.

1080
00:36:14,480 --> 00:36:15,640
They are legal requirements.

1081
00:36:15,640 --> 00:36:17,480
Violate them and you don't just lose a contract.

1082
00:36:17,480 --> 00:36:18,240
You face fines.

1083
00:36:18,240 --> 00:36:19,400
You face criminal liability.

1084
00:36:19,400 --> 00:36:21,200
You face organizational collapse.

1085
00:36:21,200 --> 00:36:23,520
Now imagine what happens when you don't have a landing zone.

1086
00:36:23,520 --> 00:36:26,520
A team builds an agent that processes customer records.

1087
00:36:26,520 --> 00:36:28,920
The agent is built in Azure running in North America.

1088
00:36:28,920 --> 00:36:29,800
It works great.

1089
00:36:29,800 --> 00:36:33,920
But then it needs to analyze trends against a data set that lives in a European warehouse.

1090
00:36:33,920 --> 00:36:36,440
The agent makes an API call directly to that warehouse.

1091
00:36:36,440 --> 00:36:37,400
The data is retrieved.

1092
00:36:37,400 --> 00:36:38,720
The agent processes it.

1093
00:36:38,720 --> 00:36:40,040
The results are cashed locally.

1094
00:36:40,040 --> 00:36:41,560
Nobody thought about geography.

1095
00:36:41,560 --> 00:36:46,640
Six months later an auditor asks, where does this agent process personal data from EU citizens?

1096
00:36:46,640 --> 00:36:47,640
You search the logs.

1097
00:36:47,640 --> 00:36:49,200
The agent is running in North America.

1098
00:36:49,200 --> 00:36:51,320
But the data it is analyzing comes from Europe.

1099
00:36:51,320 --> 00:36:52,320
Is that compliant?

1100
00:36:52,320 --> 00:36:53,720
Actually it is unclear.

1101
00:36:53,720 --> 00:36:58,000
GDPR says personal data can't be processed outside the EU unless there is a data processing

1102
00:36:58,000 --> 00:36:59,000
agreement in place.

1103
00:36:59,000 --> 00:37:00,000
Do you have one?

1104
00:37:00,000 --> 00:37:01,000
You don't know.

1105
00:37:01,000 --> 00:37:03,160
The agent was built without compliance review.

1106
00:37:03,160 --> 00:37:05,080
Now multiply this across 50 agents.

1107
00:37:05,080 --> 00:37:06,440
Each one was built independently.

1108
00:37:06,440 --> 00:37:10,280
Each one makes independent decisions about which data sources to access and which regions

1109
00:37:10,280 --> 00:37:11,440
to process in.

1110
00:37:11,440 --> 00:37:14,200
Some agents are calling models in regions that violate your policy.

1111
00:37:14,200 --> 00:37:17,120
Some are moving data across borders that shouldn't cross borders.

1112
00:37:17,120 --> 00:37:20,080
Some are caching results in locations that aren't approved.

1113
00:37:20,080 --> 00:37:21,080
Nobody coordinated.

1114
00:37:21,080 --> 00:37:22,240
Nobody enforced a standard.

1115
00:37:22,240 --> 00:37:24,160
You can't pass an audit with that.

1116
00:37:24,160 --> 00:37:25,720
The structural problem is this.

1117
00:37:25,720 --> 00:37:28,160
Without a landing zone, data movement is reactive.

1118
00:37:28,160 --> 00:37:30,800
When agent decides it needs something, so it goes and gets it.

1119
00:37:30,800 --> 00:37:34,160
By the time you audit what happened, the violation is already in the logs.

1120
00:37:34,160 --> 00:37:36,000
You are investigating after the fact.

1121
00:37:36,000 --> 00:37:40,400
You are trying to figure out what went wrong, why it went wrong, and how to prove to a

1122
00:37:40,400 --> 00:37:42,080
regulator that you fixed it.

1123
00:37:42,080 --> 00:37:43,480
This is reactive compliance.

1124
00:37:43,480 --> 00:37:46,000
You are checking boxes after the damage is done.

1125
00:37:46,000 --> 00:37:49,440
A landing zone changes that to something much more powerful.

1126
00:37:49,440 --> 00:37:50,960
Preventive compliance.

1127
00:37:50,960 --> 00:37:54,600
Data movement is gated at the infrastructure level before the violation can happen.

1128
00:37:54,600 --> 00:37:55,800
Here is how it works.

1129
00:37:55,800 --> 00:37:58,040
Your compliance team defines a policy.

1130
00:37:58,040 --> 00:38:02,640
Agents can process customer data only in regions where that customer's data is domiciled.

1131
00:38:02,640 --> 00:38:06,160
An agent is built that needs to analyze European customer records.

1132
00:38:06,160 --> 00:38:09,040
Before that agent can execute the landing zone evaluates the policy.

1133
00:38:09,040 --> 00:38:10,600
The agent is running in North America.

1134
00:38:10,600 --> 00:38:12,560
It is trying to access European data.

1135
00:38:12,560 --> 00:38:14,240
The policy forbids that combination.

1136
00:38:14,240 --> 00:38:15,920
The landing zone blocks the request.

1137
00:38:15,920 --> 00:38:20,440
The agent can't proceed until it is reconfigured to run in a region that is compliant.

1138
00:38:20,440 --> 00:38:22,520
Policy enforced before the violation happens.

1139
00:38:22,520 --> 00:38:23,920
Not audited after.

1140
00:38:23,920 --> 00:38:25,480
This is the fundamental shift.

1141
00:38:25,480 --> 00:38:28,680
Without a landing zone, compliance is a backward looking investigation.

1142
00:38:28,680 --> 00:38:31,040
You analyze what happened and try to explain it.

1143
00:38:31,040 --> 00:38:34,600
With a landing zone, compliance is forward looking enforcement.

1144
00:38:34,600 --> 00:38:38,640
Vialations become impossible because the infrastructure prevents them before they occur.

1145
00:38:38,640 --> 00:38:41,200
The operational benefit is equally significant.

1146
00:38:41,200 --> 00:38:44,600
When a regulator asks whether your agents are compliant, you don't have to investigate.

1147
00:38:44,600 --> 00:38:45,600
You have evidence.

1148
00:38:45,600 --> 00:38:46,600
The landing zone enforces the policy.

1149
00:38:46,600 --> 00:38:47,960
And that enforcement is logged.

1150
00:38:47,960 --> 00:38:51,760
You can demonstrate that your infrastructure makes non-compliance impossible for any agent

1151
00:38:51,760 --> 00:38:52,880
that runs within it.

1152
00:38:52,880 --> 00:38:55,080
This changes the risk profile entirely.

1153
00:38:55,080 --> 00:38:58,760
I'm hoping teams remember compliance rules and configured them correctly to knowing that

1154
00:38:58,760 --> 00:39:00,920
compliance is built into the system.

1155
00:39:00,920 --> 00:39:03,200
From audit surprises to regulated certainty.

1156
00:39:03,200 --> 00:39:05,480
From governance debt to governance certainty.

1157
00:39:05,480 --> 00:39:07,680
Data residency isn't just a technical requirement.

1158
00:39:07,680 --> 00:39:11,240
It is the structural difference between organizations that can prove they are compliant

1159
00:39:11,240 --> 00:39:13,240
and organizations that hope they are.

1160
00:39:13,240 --> 00:39:15,160
Cost control and fin ops.

1161
00:39:15,160 --> 00:39:16,960
There is a problem that happens at scale.

1162
00:39:16,960 --> 00:39:19,240
Almost nobody sees it coming until it's too late.

1163
00:39:19,240 --> 00:39:21,120
And by then, it's impossible to untangle it.

1164
00:39:21,120 --> 00:39:22,200
It's about the money.

1165
00:39:22,200 --> 00:39:25,320
When you start experimenting with AI, cost feels theoretical.

1166
00:39:25,320 --> 00:39:26,400
You spin up a few agents.

1167
00:39:26,400 --> 00:39:27,400
They call models.

1168
00:39:27,400 --> 00:39:28,400
The bill comes once a month.

1169
00:39:28,400 --> 00:39:29,400
It's acceptable.

1170
00:39:29,400 --> 00:39:30,400
You ship more agents.

1171
00:39:30,400 --> 00:39:31,400
The bill grows.

1172
00:39:31,400 --> 00:39:32,600
It's still manageable.

1173
00:39:32,600 --> 00:39:35,960
But somewhere around agent 50 or 20, something changes.

1174
00:39:35,960 --> 00:39:37,760
The monthly spend crosses a line.

1175
00:39:37,760 --> 00:39:41,400
It goes from an investment to a, where is all this money going?

1176
00:39:41,400 --> 00:39:42,400
Problem.

1177
00:39:42,400 --> 00:39:45,040
And when you ask that question, you realize you don't have an answer.

1178
00:39:45,040 --> 00:39:48,720
An agent in support is calling GPT-4 for every single customer query.

1179
00:39:48,720 --> 00:39:50,600
It could use a cheaper model for most of them.

1180
00:39:50,600 --> 00:39:54,480
An agent in finance is running redundant logic that another team already solved.

1181
00:39:54,480 --> 00:39:58,600
A data team's agent is making three separate API calls to fetch one piece of info.

1182
00:39:58,600 --> 00:40:03,160
A marketing agent is hitting a model every 60 seconds just to check for updates.

1183
00:40:03,160 --> 00:40:05,480
Nobody coordinated, nobody measured, nobody optimized.

1184
00:40:05,480 --> 00:40:06,840
They just shipped.

1185
00:40:06,840 --> 00:40:09,400
Without visibility into consumption, you can't manage cost.

1186
00:40:09,400 --> 00:40:10,480
You can't even see it.

1187
00:40:10,480 --> 00:40:12,680
You get a cloud bill that's higher than last month.

1188
00:40:12,680 --> 00:40:14,200
You email the teams to ask why.

1189
00:40:14,200 --> 00:40:15,200
Nobody knows.

1190
00:40:15,200 --> 00:40:18,240
The money is disappearing into a black box of token consumption.

1191
00:40:18,240 --> 00:40:19,560
Dozens of agents are running.

1192
00:40:19,560 --> 00:40:23,720
There's no way to attribute spend to a specific team or a specific use case.

1193
00:40:23,720 --> 00:40:27,000
This is where the landing zone's model gateway becomes the solution.

1194
00:40:27,000 --> 00:40:30,160
Every single model call goes through that gateway, not to slow things down.

1195
00:40:30,160 --> 00:40:34,000
But because that gateway is the one place where you can see and control access, which agent

1196
00:40:34,000 --> 00:40:35,000
called which model.

1197
00:40:35,000 --> 00:40:36,400
How many tokens did it use?

1198
00:40:36,400 --> 00:40:37,960
What region did the call go to?

1199
00:40:37,960 --> 00:40:40,240
This data flows into a cost tracking system.

1200
00:40:40,240 --> 00:40:44,840
Not monthly, not quarterly, continuously, real time visibility.

1201
00:40:44,840 --> 00:40:47,240
Now, cost management is actually possible.

1202
00:40:47,240 --> 00:40:50,920
You can see that one single agent accounts for 30% of your spend.

1203
00:40:50,920 --> 00:40:52,880
You can drill down and understand why.

1204
00:40:52,880 --> 00:40:55,840
Is it calling an expensive model when a cheap one would work?

1205
00:40:55,840 --> 00:40:58,360
Is it asking for a longer context window than it needs?

1206
00:40:58,360 --> 00:40:59,720
Is it making redundant calls?

1207
00:40:59,720 --> 00:41:01,360
Once you see the pattern, you can fix it.

1208
00:41:01,360 --> 00:41:04,560
Maybe that agent switches to a smaller model for routine questions.

1209
00:41:04,560 --> 00:41:07,680
It only escalates to the expensive model when it's actually needed.

1210
00:41:07,680 --> 00:41:11,080
Maybe you add caching so it stops asking the same thing over and over.

1211
00:41:11,080 --> 00:41:13,400
Maybe you redesigned the prompt to use fewer tokens.

1212
00:41:13,400 --> 00:41:14,600
But here's the critical part.

1213
00:41:14,600 --> 00:41:18,040
You can do this optimization without losing any capability.

1214
00:41:18,040 --> 00:41:20,840
Without visibility, the choices binary, the agent works.

1215
00:41:20,840 --> 00:41:21,840
Or it doesn't.

1216
00:41:21,840 --> 00:41:22,840
There's nothing in between.

1217
00:41:22,840 --> 00:41:24,240
With visibility, you can optimize.

1218
00:41:24,240 --> 00:41:26,760
You keep the same outcome at a much lower cost.

1219
00:41:26,760 --> 00:41:30,920
That's the difference between paying $60,000 a month and paying $20,000 for the exact same

1220
00:41:30,920 --> 00:41:31,920
result.

1221
00:41:31,920 --> 00:41:33,960
The gateway also enables model routing.

1222
00:41:33,960 --> 00:41:35,320
Different models have different costs.

1223
00:41:35,320 --> 00:41:38,720
An expensive model might be brilliant, but it's overkill for most tasks.

1224
00:41:38,720 --> 00:41:41,280
A cheaper model handles routine work just fine.

1225
00:41:41,280 --> 00:41:44,600
But a gateway every agent decides on its own which model to use.

1226
00:41:44,600 --> 00:41:47,840
With a gateway, policy-based routing makes that decision for them.

1227
00:41:47,840 --> 00:41:49,240
An agent submits a request.

1228
00:41:49,240 --> 00:41:51,240
The gateway evaluates it based on your rules.

1229
00:41:51,240 --> 00:41:54,120
If it's a simple question, root it to the cheap model.

1230
00:41:54,120 --> 00:41:56,960
If it needs deep reasoning, root it to the expensive one.

1231
00:41:56,960 --> 00:42:01,240
Same agent, different models depending on the actual need, cost is optimized, performance

1232
00:42:01,240 --> 00:42:02,960
is maintained.

1233
00:42:02,960 --> 00:42:04,800
Budget controls finally become real.

1234
00:42:04,800 --> 00:42:07,400
You set a monthly budget for a team or a project.

1235
00:42:07,400 --> 00:42:11,480
As agents use tokens, the spend is tied to that budget when they hit 80%.

1236
00:42:11,480 --> 00:42:12,480
You get an alert.

1237
00:42:12,480 --> 00:42:14,320
At 100%, the call stops.

1238
00:42:14,320 --> 00:42:15,520
This forces a conversation.

1239
00:42:15,520 --> 00:42:17,240
Why did this team use their whole budget?

1240
00:42:17,240 --> 00:42:18,240
Was it necessary?

1241
00:42:18,240 --> 00:42:19,240
Is there waste?

1242
00:42:19,240 --> 00:42:21,080
Or is this a success that we need to scale up?

1243
00:42:21,080 --> 00:42:23,560
You can't have these conversations without attribution.

1244
00:42:23,560 --> 00:42:24,560
With it.

1245
00:42:24,560 --> 00:42:27,360
Budget management is how you align AI spending with business value.

1246
00:42:27,360 --> 00:42:28,520
The logic is clear.

1247
00:42:28,520 --> 00:42:32,280
Without a landing zone, cost grows randomly as teams ship, with one.

1248
00:42:32,280 --> 00:42:33,520
Cost is a controlled variable.

1249
00:42:33,520 --> 00:42:34,960
You understand where the money goes.

1250
00:42:34,960 --> 00:42:36,400
You optimize what wastes it.

1251
00:42:36,400 --> 00:42:37,880
You scale what works.

1252
00:42:37,880 --> 00:42:41,040
Finops stops being something you do after a shocking bill arrives.

1253
00:42:41,040 --> 00:42:42,960
It becomes how you operate from day one.

1254
00:42:42,960 --> 00:42:44,200
Cost isn't just a finance problem.

1255
00:42:44,200 --> 00:42:45,560
It's a structural problem.

1256
00:42:45,560 --> 00:42:47,200
And the landing zone solves it.

1257
00:42:47,200 --> 00:42:49,080
The ontology advantage in practice.

1258
00:42:49,080 --> 00:42:50,880
We've talked about ontologies in the abstract.

1259
00:42:50,880 --> 00:42:54,200
Now let's talk about what they actually do in a production system.

1260
00:42:54,200 --> 00:42:57,200
An ontology encodes relationships that are usually invisible.

1261
00:42:57,200 --> 00:42:59,480
Your data warehouse has a table of customers.

1262
00:42:59,480 --> 00:43:02,480
A column says segment with a value like enterprise.

1263
00:43:02,480 --> 00:43:03,480
That is a fact.

1264
00:43:03,480 --> 00:43:05,200
An ontology says the same thing differently.

1265
00:43:05,200 --> 00:43:06,760
This customer is a VIP.

1266
00:43:06,760 --> 00:43:08,240
That means their orders get priority.

1267
00:43:08,240 --> 00:43:10,680
That means a delay triggers an immediate escalation.

1268
00:43:10,680 --> 00:43:13,360
That means the operations team gets in a lurch right now.

1269
00:43:13,360 --> 00:43:16,040
Instead of finding out next week, those sound like the same thing.

1270
00:43:16,040 --> 00:43:17,040
They are not.

1271
00:43:17,040 --> 00:43:18,040
The table is data.

1272
00:43:18,040 --> 00:43:19,040
The ontology is meaning.

1273
00:43:19,040 --> 00:43:20,200
One is a field in a record.

1274
00:43:20,200 --> 00:43:22,840
The other is a relationship that carries a consequence.

1275
00:43:22,840 --> 00:43:25,720
When an agent knows a customer segment, it has information.

1276
00:43:25,720 --> 00:43:30,720
When an agent understands how VIP status changes in operational decision, it has context.

1277
00:43:30,720 --> 00:43:35,040
Context is what separates an agent that answers questions from an agent that makes decisions.

1278
00:43:35,040 --> 00:43:37,560
And this is where it becomes operationally significant.

1279
00:43:37,560 --> 00:43:39,400
Agents reason across the relationships that matter.

1280
00:43:39,400 --> 00:43:41,000
Not every possible relationship.

1281
00:43:41,000 --> 00:43:43,160
Think about an agent monitoring in order.

1282
00:43:43,160 --> 00:43:45,280
Without an ontology, its reasoning is a mess.

1283
00:43:45,280 --> 00:43:46,440
The customer placed in order.

1284
00:43:46,440 --> 00:43:47,720
The order has products.

1285
00:43:47,720 --> 00:43:48,960
Products need inventory.

1286
00:43:48,960 --> 00:43:50,240
Inventory is in a warehouse.

1287
00:43:50,240 --> 00:43:51,560
The warehouse has a supplier.

1288
00:43:51,560 --> 00:43:52,760
The supplier might be late.

1289
00:43:52,760 --> 00:43:54,680
That's a dozen different relationships to analyze.

1290
00:43:54,680 --> 00:43:55,680
Which one's matter right now?

1291
00:43:55,680 --> 00:43:57,080
The agent has to guess.

1292
00:43:57,080 --> 00:43:58,440
Maybe it looks at lead times.

1293
00:43:58,440 --> 00:43:59,880
Maybe it looks at stock levels.

1294
00:43:59,880 --> 00:44:02,800
Maybe it checks payment history because it thinks that matters.

1295
00:44:02,800 --> 00:44:06,000
The agent might miss the one link that actually determines priority.

1296
00:44:06,000 --> 00:44:09,640
Or it might waste compute looking at things that don't matter at all.

1297
00:44:09,640 --> 00:44:10,640
With an ontology.

1298
00:44:10,640 --> 00:44:11,960
The reasoning is structured.

1299
00:44:11,960 --> 00:44:14,320
The relationships that matter are already encoded.

1300
00:44:14,320 --> 00:44:16,160
VIP customers get priority.

1301
00:44:16,160 --> 00:44:17,920
Peak season increases that priority.

1302
00:44:17,920 --> 00:44:19,320
Certain suppliers are high-risk.

1303
00:44:19,320 --> 00:44:22,360
High-risk suppliers in peak season require an acceleration plan.

1304
00:44:22,360 --> 00:44:23,200
The agent doesn't guess.

1305
00:44:23,200 --> 00:44:25,560
It follows a path that has already been thought through.

1306
00:44:25,560 --> 00:44:28,520
It follows relationships that were explicitly defined as relevant.

1307
00:44:28,520 --> 00:44:30,960
The hallucination prevention here isn't about rules.

1308
00:44:30,960 --> 00:44:32,480
It's structural.

1309
00:44:32,480 --> 00:44:35,880
An agent can't invent a relationship that doesn't exist in the ontology.

1310
00:44:35,880 --> 00:44:37,320
It doesn't have the freedom to do that.

1311
00:44:37,320 --> 00:44:39,080
The ontology isn't a suggestion.

1312
00:44:39,080 --> 00:44:40,080
It's a boundary.

1313
00:44:40,080 --> 00:44:41,920
If an agent needs to reason, it has to follow a path.

1314
00:44:41,920 --> 00:44:44,000
If there is no path, it can't reason about it.

1315
00:44:44,000 --> 00:44:45,240
And that is exactly what you want.

1316
00:44:45,240 --> 00:44:48,080
You don't want an agent making up logic you haven't approved.

1317
00:44:48,080 --> 00:44:51,800
This scales in a way that documents never will when you have ten agents.

1318
00:44:51,800 --> 00:44:53,440
They could all be reasoning differently.

1319
00:44:53,440 --> 00:44:55,720
One agent defines VIP status one way.

1320
00:44:55,720 --> 00:44:57,200
Another agent defines it another way.

1321
00:44:57,200 --> 00:44:58,880
One prioritizes inventory.

1322
00:44:58,880 --> 00:45:00,720
One prioritizes speed.

1323
00:45:00,720 --> 00:45:04,840
You get inconsistent decisions across the company because every agent built its own mental

1324
00:45:04,840 --> 00:45:05,840
model.

1325
00:45:05,840 --> 00:45:07,800
With an ontology.

1326
00:45:07,800 --> 00:45:09,080
Consistency is structural.

1327
00:45:09,080 --> 00:45:11,840
All ten agents see the same definition of VIP.

1328
00:45:11,840 --> 00:45:15,080
All ten use the same criteria for supply or risk.

1329
00:45:15,080 --> 00:45:17,280
All ten optimize against the same business logic.

1330
00:45:17,280 --> 00:45:18,360
You don't have to train them.

1331
00:45:18,360 --> 00:45:20,840
You don't have to write policies and hope they understand.

1332
00:45:20,840 --> 00:45:22,280
The ontology is the policy.

1333
00:45:22,280 --> 00:45:23,600
It is executable.

1334
00:45:23,600 --> 00:45:24,760
It is consistent.

1335
00:45:24,760 --> 00:45:26,480
It is auditable.

1336
00:45:26,480 --> 00:45:28,960
And here is the shift that nobody anticipates.

1337
00:45:28,960 --> 00:45:31,680
An agent stops being about stopping bad outputs.

1338
00:45:31,680 --> 00:45:33,440
It becomes about enabling good reasoning.

1339
00:45:33,440 --> 00:45:34,760
Without an ontology.

1340
00:45:34,760 --> 00:45:35,760
Governance is reactive.

1341
00:45:35,760 --> 00:45:37,080
An agent makes a mistake.

1342
00:45:37,080 --> 00:45:38,080
You review it.

1343
00:45:38,080 --> 00:45:39,080
You block it.

1344
00:45:39,080 --> 00:45:40,880
You tell the agent, don't do that.

1345
00:45:40,880 --> 00:45:42,400
But you're just policing the output.

1346
00:45:42,400 --> 00:45:44,360
You aren't changing how the agent thinks.

1347
00:45:44,360 --> 00:45:46,440
With an ontology, governance is proactive.

1348
00:45:46,440 --> 00:45:47,760
Before the agent ever runs.

1349
00:45:47,760 --> 00:45:49,520
You've defined how it should reason.

1350
00:45:49,520 --> 00:45:50,520
You've encoded the logic.

1351
00:45:50,520 --> 00:45:51,840
You've constrained the paths.

1352
00:45:51,840 --> 00:45:55,880
The agent can't make an unreasonable decision because making a decision requires following

1353
00:45:55,880 --> 00:45:57,440
the ontology.

1354
00:45:57,440 --> 00:46:00,920
And the ontology only contains the paths you've decided are reasonable.

1355
00:46:00,920 --> 00:46:03,120
This is the shift from control to enablement.

1356
00:46:03,120 --> 00:46:04,920
You aren't restricting what agents can do.

1357
00:46:04,920 --> 00:46:07,080
You are giving them the right way to think.

1358
00:46:07,080 --> 00:46:08,880
Multi-cloud agent orchestration.

1359
00:46:08,880 --> 00:46:11,560
Most organizations don't run everything on one cloud.

1360
00:46:11,560 --> 00:46:14,040
If they did, the governance problem would be simpler.

1361
00:46:14,040 --> 00:46:17,480
You'd have one control plane, one set of policies, one identity system.

1362
00:46:17,480 --> 00:46:19,560
Everything would be integrated.

1363
00:46:19,560 --> 00:46:21,080
But that's not the world we live in.

1364
00:46:21,080 --> 00:46:24,800
You have a co-pilot studio agent built by the support team running in Azure.

1365
00:46:24,800 --> 00:46:29,160
You have an AWS bedrock agent built by the data team running in their separate cloud account.

1366
00:46:29,160 --> 00:46:32,120
You have an open source framework agent that a contractor built.

1367
00:46:32,120 --> 00:46:35,520
And it's now running on Kubernetes in your on-premises data center.

1368
00:46:35,520 --> 00:46:39,680
You have a SAS agent from a third-party vendor running in their cloud, not yours.

1369
00:46:39,680 --> 00:46:41,040
Each one does something useful.

1370
00:46:41,040 --> 00:46:43,560
Each one was the right choice for the team that built it.

1371
00:46:43,560 --> 00:46:48,160
But together, they create a governance problem that no single cloud provider can solve.

1372
00:46:48,160 --> 00:46:51,680
Because without a unified control plane, governance ends up in multiple places.

1373
00:46:51,680 --> 00:46:52,680
Or nowhere.

1374
00:46:52,680 --> 00:46:56,160
Azure agent has an enter identity because it's in the Microsoft ecosystem.

1375
00:46:56,160 --> 00:46:59,240
But does the AWS agent have those same identity standards applied?

1376
00:46:59,240 --> 00:47:00,240
Not really.

1377
00:47:00,240 --> 00:47:01,240
AWS uses IAM roles.

1378
00:47:01,240 --> 00:47:02,240
They aren't the same thing.

1379
00:47:02,240 --> 00:47:05,800
The SAS agent might use whatever authentication the vendor decided on.

1380
00:47:05,800 --> 00:47:10,120
While the open source agent might be using keys that nobody really knows how to rotate securely,

1381
00:47:10,120 --> 00:47:13,840
you're looking at four different identity models, four different access control patterns,

1382
00:47:13,840 --> 00:47:15,360
and four different audit trails.

1383
00:47:15,360 --> 00:47:18,040
Now, try to enforce a policy across all of them.

1384
00:47:18,040 --> 00:47:21,960
Let's say your compliance team decides that agents can't call models that run outside

1385
00:47:21,960 --> 00:47:23,080
the United States.

1386
00:47:23,080 --> 00:47:27,560
The Azure agent uses Azure OpenAI, which is easy to restrict the AWS agent connects to

1387
00:47:27,560 --> 00:47:29,920
Bedrock, which is also relatively straightforward.

1388
00:47:29,920 --> 00:47:33,000
But the SAS agent is calling a European provider's API.

1389
00:47:33,000 --> 00:47:36,240
The open source agent is using a model that someone deployed in Google Cloud.

1390
00:47:36,240 --> 00:47:38,680
You can't control those from an Azure policy engine.

1391
00:47:38,680 --> 00:47:39,760
They aren't in your cloud.

1392
00:47:39,760 --> 00:47:41,480
They aren't in your identity system.

1393
00:47:41,480 --> 00:47:43,440
The result is a fragmented governance model.

1394
00:47:43,440 --> 00:47:45,360
Some policies you can enforce technically.

1395
00:47:45,360 --> 00:47:47,040
Others you enforce through documentation.

1396
00:47:47,040 --> 00:47:48,520
Others you just hope nobody violates.

1397
00:47:48,520 --> 00:47:50,120
This is the fragmentation problem.

1398
00:47:50,120 --> 00:47:54,280
As soon as your agents cross cloud boundaries, centralized governance becomes exponentially

1399
00:47:54,280 --> 00:47:55,280
harder.

1400
00:47:55,280 --> 00:47:57,320
The challenge isn't that cloud providers are bad at governance.

1401
00:47:57,320 --> 00:47:59,360
It's that they're only good at governing their own cloud.

1402
00:47:59,360 --> 00:48:01,120
They can't govern beyond their boundaries.

1403
00:48:01,120 --> 00:48:04,920
This is where agent 365 and the model context protocol become essential.

1404
00:48:04,920 --> 00:48:07,440
Agent 365 operates above any specific cloud.

1405
00:48:07,440 --> 00:48:11,480
It doesn't care whether an agent is running on Azure or AWS or on your own servers.

1406
00:48:11,480 --> 00:48:13,880
It can register agents regardless of where they run.

1407
00:48:13,880 --> 00:48:17,600
It can assign them identities that stay consistent regardless of the cloud.

1408
00:48:17,600 --> 00:48:21,080
They can apply policies that work across clouds because those policies are enforced at the

1409
00:48:21,080 --> 00:48:24,080
agent 365 layer, not at the cloud provider layer.

1410
00:48:24,080 --> 00:48:27,680
The model context protocol does something similar for tools and data sources.

1411
00:48:27,680 --> 00:48:32,360
MCP is a standard that lets agents connect to tools and data regardless of where those tools

1412
00:48:32,360 --> 00:48:33,360
live.

1413
00:48:33,360 --> 00:48:37,200
Instead of each agent negotiating its own connection to each tool, agents connect to tools

1414
00:48:37,200 --> 00:48:38,200
through MCP.

1415
00:48:38,200 --> 00:48:42,600
That means the governance hub can define which tools are approved and which aren't.

1416
00:48:42,600 --> 00:48:47,000
And that decision applies whether the tool is running on Azure, AWS or anywhere else.

1417
00:48:47,000 --> 00:48:50,120
Here's what this looks like in practice.

1418
00:48:50,120 --> 00:48:53,120
Your organization has agents on four different clouds.

1419
00:48:53,120 --> 00:48:57,240
A compliance policy gets created stating that agents can't connect to tools that aren't

1420
00:48:57,240 --> 00:49:00,880
in the approved catalog that policy lives in agent 365.

1421
00:49:00,880 --> 00:49:05,840
Every agent regardless of where it runs validates its tool connections through agent 365.

1422
00:49:05,840 --> 00:49:09,640
An agent tries to invoke a tool agent 365 checks the tool against the policy.

1423
00:49:09,640 --> 00:49:10,640
Is it approved?

1424
00:49:10,640 --> 00:49:13,640
If yes, the agent can call it if no, the request is blocked.

1425
00:49:13,640 --> 00:49:18,120
This works for the Azure agent, the AWS agent, the on-premises agent and the SAS agent,

1426
00:49:18,120 --> 00:49:20,840
same policy, same enforcement, different clouds.

1427
00:49:20,840 --> 00:49:23,640
The operational benefit is that you're not locked into one vendor.

1428
00:49:23,640 --> 00:49:27,280
You can choose the best runtime for each job without sacrificing governance.

1429
00:49:27,280 --> 00:49:28,600
Need a specialized service?

1430
00:49:28,600 --> 00:49:30,080
Use a third party SAS?

1431
00:49:30,080 --> 00:49:31,360
Need cost efficiency?

1432
00:49:31,360 --> 00:49:33,280
Use spot instances on AWS?

1433
00:49:33,280 --> 00:49:34,880
Need deep Azure integration?

1434
00:49:34,880 --> 00:49:36,040
Use Azure.

1435
00:49:36,040 --> 00:49:37,840
Each decision is made independently.

1436
00:49:37,840 --> 00:49:40,920
Governance is still unified because agent 365 sits above all of them.

1437
00:49:40,920 --> 00:49:42,960
This is what vendor flexibility looks like.

1438
00:49:42,960 --> 00:49:46,640
It's not being forced to choose one cloud because that's the only way to govern.

1439
00:49:46,640 --> 00:49:50,480
It's being able to choose based on actual capability while maintaining a single governance

1440
00:49:50,480 --> 00:49:52,160
layer across everything.

1441
00:49:52,160 --> 00:49:57,480
That flexibility is what lets the landing zone scale across an organization's entire AI footprint.

1442
00:49:57,480 --> 00:49:59,560
Life cycle management and ghost agents.

1443
00:49:59,560 --> 00:50:03,400
Here's something that happens in every organization that scales agents to a certain point.

1444
00:50:03,400 --> 00:50:04,400
Nobody plans for it.

1445
00:50:04,400 --> 00:50:05,400
Nobody expects it.

1446
00:50:05,400 --> 00:50:09,240
But by the time you notice, it's already a problem, an agent gets built, a team has a three-month

1447
00:50:09,240 --> 00:50:10,240
project.

1448
00:50:10,240 --> 00:50:11,240
They need automation.

1449
00:50:11,240 --> 00:50:12,240
They request an agent.

1450
00:50:12,240 --> 00:50:13,240
It works.

1451
00:50:13,240 --> 00:50:14,240
The project ends.

1452
00:50:14,240 --> 00:50:15,240
The team moves on.

1453
00:50:15,240 --> 00:50:16,240
The agent is still there.

1454
00:50:16,240 --> 00:50:17,240
Still running.

1455
00:50:17,240 --> 00:50:18,240
Still consuming tokens.

1456
00:50:18,240 --> 00:50:19,240
Still holding onto access.

1457
00:50:19,240 --> 00:50:20,240
It no longer needs.

1458
00:50:20,240 --> 00:50:21,240
Six months later.

1459
00:50:21,240 --> 00:50:22,880
Nobody remembers what it was for.

1460
00:50:22,880 --> 00:50:24,600
The person who built it left the company.

1461
00:50:24,600 --> 00:50:27,000
The original sponsor has moved to a different role.

1462
00:50:27,000 --> 00:50:29,240
The project might not even exist anymore.

1463
00:50:29,240 --> 00:50:30,720
But the agent keeps running.

1464
00:50:30,720 --> 00:50:31,720
It's invisible.

1465
00:50:31,720 --> 00:50:35,160
Until you look at your monthly cloud bill and wonder why it's higher than expected.

1466
00:50:35,160 --> 00:50:37,200
You trace the spend and find this ghost.

1467
00:50:37,200 --> 00:50:38,400
It's costing you money.

1468
00:50:38,400 --> 00:50:39,880
It's accessing data.

1469
00:50:39,880 --> 00:50:41,440
Nobody authorized it to access anymore.

1470
00:50:41,440 --> 00:50:45,440
It might be using models or tools that your organization no longer approves.

1471
00:50:45,440 --> 00:50:47,600
It's a security risk nobody knew existed.

1472
00:50:47,600 --> 00:50:51,560
Now multiply that by 50 or 100 as organizations build more agents.

1473
00:50:51,560 --> 00:50:53,800
The graveyard of forgotten projects grows.

1474
00:50:53,800 --> 00:50:55,080
These aren't catastrophic failures.

1475
00:50:55,080 --> 00:50:56,680
They aren't costing you millions.

1476
00:50:56,680 --> 00:50:58,560
But they're the death of a thousand cuts.

1477
00:50:58,560 --> 00:50:59,560
Waste it spend.

1478
00:50:59,560 --> 00:51:00,920
Slow erosion of governance.

1479
00:51:00,920 --> 00:51:05,560
Accumulation of risk that nobody's actively managing because the agents are just there.

1480
00:51:05,560 --> 00:51:07,960
This is where life cycle governance becomes critical.

1481
00:51:07,960 --> 00:51:11,760
Not as a nice to have compliance exercise, but as an operational necessity.

1482
00:51:11,760 --> 00:51:14,960
Without life cycle management agents are created but never retired.

1483
00:51:14,960 --> 00:51:15,960
They exist in a weird limbo.

1484
00:51:15,960 --> 00:51:17,440
They're still running.

1485
00:51:17,440 --> 00:51:18,520
Still consuming resources.

1486
00:51:18,520 --> 00:51:20,240
Still holding permissions.

1487
00:51:20,240 --> 00:51:21,960
But nobody claims responsibility for them.

1488
00:51:21,960 --> 00:51:23,680
The person who sponsored them is gone.

1489
00:51:23,680 --> 00:51:25,360
The team that built them has moved on.

1490
00:51:25,360 --> 00:51:26,800
Nobody knows if they're still needed.

1491
00:51:26,800 --> 00:51:28,960
Nobody knows who should decide whether to turn them off.

1492
00:51:28,960 --> 00:51:30,440
So they just keep running.

1493
00:51:30,440 --> 00:51:33,840
Agent 365 solves this by making the life cycle explicit.

1494
00:51:33,840 --> 00:51:37,800
When an agent registers in the control plane, it doesn't just get an identity and permissions.

1495
00:51:37,800 --> 00:51:39,000
It gets an owner.

1496
00:51:39,000 --> 00:51:40,000
That owner is accountable.

1497
00:51:40,000 --> 00:51:43,680
They're responsible for maintaining that agent, reviewing its access and ensuring it's

1498
00:51:43,680 --> 00:51:45,560
still doing what it's supposed to do.

1499
00:51:45,560 --> 00:51:47,760
But ownership alone doesn't prevent ghost agents.

1500
00:51:47,760 --> 00:51:48,760
You need visibility.

1501
00:51:48,760 --> 00:51:50,720
Agent 365 tracks usage.

1502
00:51:50,720 --> 00:51:52,600
Every call the agent makes gets logged.

1503
00:51:52,600 --> 00:51:54,640
Not just what it does, but how often it does it.

1504
00:51:54,640 --> 00:51:57,280
You can see if an agent hasn't been called in six months.

1505
00:51:57,280 --> 00:51:59,360
You can see if its permissions haven't been reviewed in a year.

1506
00:51:59,360 --> 00:52:00,760
You can see patterns of decay.

1507
00:52:00,760 --> 00:52:04,200
An agent that used to be called a hundred times a day now gets called once a week.

1508
00:52:04,200 --> 00:52:05,200
Something changed.

1509
00:52:05,200 --> 00:52:06,840
Either the business need went away.

1510
00:52:06,840 --> 00:52:08,840
Or the agent broke and nobody noticed.

1511
00:52:08,840 --> 00:52:12,240
This visibility enables decisions that are otherwise impossible.

1512
00:52:12,240 --> 00:52:14,200
You can't retire something you don't know is running.

1513
00:52:14,200 --> 00:52:16,160
You can't optimize something you can't see.

1514
00:52:16,160 --> 00:52:18,080
But with Agent 365, you have the data.

1515
00:52:18,080 --> 00:52:21,200
An owner gets notified that their agent hasn't been used in 90 days.

1516
00:52:21,200 --> 00:52:24,120
They have to decide, does this agent still serve a purpose?

1517
00:52:24,120 --> 00:52:25,760
If yes, they recertify it.

1518
00:52:25,760 --> 00:52:27,720
If no, they request retirement.

1519
00:52:27,720 --> 00:52:28,720
That's the governance model.

1520
00:52:28,720 --> 00:52:29,720
Not force.

1521
00:52:29,720 --> 00:52:30,720
Not threats.

1522
00:52:30,720 --> 00:52:32,640
Clarity and accountability.

1523
00:52:32,640 --> 00:52:34,800
The retirement process itself becomes structured.

1524
00:52:34,800 --> 00:52:36,520
An agent gets marked for retirement.

1525
00:52:36,520 --> 00:52:39,880
There's a grace period where the team can appeal or recertify it.

1526
00:52:39,880 --> 00:52:42,080
After the grace period, access gets revoked.

1527
00:52:42,080 --> 00:52:43,880
The agent can't call models anymore.

1528
00:52:43,880 --> 00:52:45,120
It can't access data.

1529
00:52:45,120 --> 00:52:46,200
It's still in the system.

1530
00:52:46,200 --> 00:52:48,280
You keep records for audit purposes.

1531
00:52:48,280 --> 00:52:49,360
But it's no longer active.

1532
00:52:49,360 --> 00:52:50,520
Costs stops accumulating.

1533
00:52:50,520 --> 00:52:51,520
Risk stops growing.

1534
00:52:51,520 --> 00:52:53,520
The resources it was holding are freed up.

1535
00:52:53,520 --> 00:52:54,880
Here's what's significant about this.

1536
00:52:54,880 --> 00:52:56,280
It's not a one-time cleanup.

1537
00:52:56,280 --> 00:52:57,560
It's an ongoing process.

1538
00:52:57,560 --> 00:52:59,200
Every agent has a life cycle.

1539
00:52:59,200 --> 00:53:00,200
Creation.

1540
00:53:00,200 --> 00:53:01,200
Active use.

1541
00:53:01,200 --> 00:53:02,200
Review.

1542
00:53:02,200 --> 00:53:03,200
Retirement.

1543
00:53:03,200 --> 00:53:05,680
Organizations that build sustainable AI practices treat agent life cycle the way they

1544
00:53:05,680 --> 00:53:07,880
treat employee life cycle.

1545
00:53:07,880 --> 00:53:09,840
Onboarding with clear expectations.

1546
00:53:09,840 --> 00:53:10,680
Regular check-ins.

1547
00:53:10,680 --> 00:53:11,680
Annual reviews.

1548
00:53:11,680 --> 00:53:13,120
Offboarding when the need ends.

1549
00:53:13,120 --> 00:53:15,480
The financial impact is measurable.

1550
00:53:15,480 --> 00:53:19,520
Organizations that implement life cycle governance report 15 to 30% cost reduction from

1551
00:53:19,520 --> 00:53:23,120
shutting down ghost agents and decommissioning redundant ones.

1552
00:53:23,120 --> 00:53:24,880
But the security impact is more important.

1553
00:53:24,880 --> 00:53:27,920
Every retired agent is a surface you no longer have to monitor.

1554
00:53:27,920 --> 00:53:31,400
Every unused permission you revoke is a tax surface you've eliminated.

1555
00:53:31,400 --> 00:53:34,520
Every ghost you eliminate is a potential liability you've erased.

1556
00:53:34,520 --> 00:53:37,840
The shift here is from treating agents as permanent fixtures.

1557
00:53:37,840 --> 00:53:41,480
To treating them as temporary solutions with built-in expiration dates, that's what responsible

1558
00:53:41,480 --> 00:53:43,720
AI operations looks like.

1559
00:53:43,720 --> 00:53:45,080
The observability layer.

1560
00:53:45,080 --> 00:53:46,720
You can't govern what you can't see.

1561
00:53:46,720 --> 00:53:49,160
That is the principle that ties this entire system together.

1562
00:53:49,160 --> 00:53:50,320
You've built the identity.

1563
00:53:50,320 --> 00:53:51,840
You've built the policy enforcement.

1564
00:53:51,840 --> 00:53:53,520
You've built the life cycle management.

1565
00:53:53,520 --> 00:53:57,160
But if you can't trace what actually happened, you're still flying blind.

1566
00:53:57,160 --> 00:53:59,840
Observability is the thread that connects every decision an agent makes back to the

1567
00:53:59,840 --> 00:54:01,240
context that prompted it.

1568
00:54:01,240 --> 00:54:02,480
It's not just logging.

1569
00:54:02,480 --> 00:54:04,480
Logging is recording that something happened.

1570
00:54:04,480 --> 00:54:06,480
Observability is understanding why it happened.

1571
00:54:06,480 --> 00:54:07,880
Who initiated the request?

1572
00:54:07,880 --> 00:54:09,200
What data informed the choice?

1573
00:54:09,200 --> 00:54:10,840
What were the actual consequences?

1574
00:54:10,840 --> 00:54:14,120
When you have true observability and agent's action is never isolated.

1575
00:54:14,120 --> 00:54:17,840
It is always traceable back through the entire chain of events that led to that specific

1576
00:54:17,840 --> 00:54:18,840
moment.

1577
00:54:18,840 --> 00:54:19,920
Here's the problem in practice.

1578
00:54:19,920 --> 00:54:23,920
An agent in your organization makes a recommendation that turns out to be wrong.

1579
00:54:23,920 --> 00:54:26,640
A customer acts on that bad advice and loses money.

1580
00:54:26,640 --> 00:54:30,720
Now you have a massive problem and you need to understand exactly what broke.

1581
00:54:30,720 --> 00:54:33,120
Without observability, you're stuck.

1582
00:54:33,120 --> 00:54:35,800
You know the output was incorrect, but you have no idea why.

1583
00:54:35,800 --> 00:54:37,600
Was it using stale data from the warehouse?

1584
00:54:37,600 --> 00:54:39,280
Did the ontology have a structural flaw?

1585
00:54:39,280 --> 00:54:41,960
Did the agent follow a path that shouldn't have been able to access?

1586
00:54:41,960 --> 00:54:43,040
You're just guessing.

1587
00:54:43,040 --> 00:54:45,880
But with observability, you trace the logic backward.

1588
00:54:45,880 --> 00:54:50,040
You see the exact prompt that triggered the agent and the specific data sources it consulted.

1589
00:54:50,040 --> 00:54:54,000
You see which relationships in the ontology it traversed and which policy decisions the

1590
00:54:54,000 --> 00:54:56,040
gateway made before calling the model.

1591
00:54:56,040 --> 00:54:57,680
You see the intermediate reasoning steps.

1592
00:54:57,680 --> 00:54:59,600
You see where the logic actually broke down.

1593
00:54:59,600 --> 00:55:03,080
The result is that you understand not just that something went wrong, but where

1594
00:55:03,080 --> 00:55:04,080
and why.

1595
00:55:04,080 --> 00:55:07,800
The architecture here is a centralized system that ties every single event together.

1596
00:55:07,800 --> 00:55:10,840
Every agent action gets logged with its full context attached.

1597
00:55:10,840 --> 00:55:14,600
The agent's identity, the user who triggered it, the data it accessed, the policies that

1598
00:55:14,600 --> 00:55:18,960
were evaluated, all of this flows into one place where it can be analyzed.

1599
00:55:18,960 --> 00:55:21,920
This is fundamentally different from standard application logging.

1600
00:55:21,920 --> 00:55:25,840
A typical app logs simple actions like a user clicking a button or a page loading.

1601
00:55:25,840 --> 00:55:28,720
An agent observability system logs the entire decision chain.

1602
00:55:28,720 --> 00:55:33,960
It shows that a specific analysis agent was called by a specific user at 247 pm.

1603
00:55:33,960 --> 00:55:39,000
It records that the system evaluated a policy for sales roles, satisfied that policy, and

1604
00:55:39,000 --> 00:55:41,560
then queered the ontology for high value customers.

1605
00:55:41,560 --> 00:55:46,200
It shows the agent checked order history, invoked a pricing model, and returned 12 candidates

1606
00:55:46,200 --> 00:55:47,760
with specific confidence scores.

1607
00:55:47,760 --> 00:55:49,080
It even tracks the cost.

1608
00:55:49,080 --> 00:55:53,680
Noting that the 0.47 tokens used should be built to sales operations.

1609
00:55:53,680 --> 00:55:55,400
That is a massive amount of information.

1610
00:55:55,400 --> 00:55:58,040
And because it's all connected, you can verify the logic.

1611
00:55:58,040 --> 00:56:01,760
If a customer was excluded from a list, you can see exactly which rule bumped them out.

1612
00:56:01,760 --> 00:56:03,240
This changes incident response.

1613
00:56:03,240 --> 00:56:07,280
In organizations without this layer, investigations are slow because fragments of info are scattered

1614
00:56:07,280 --> 00:56:10,640
across cloud logs, database logs, and policy logs.

1615
00:56:10,640 --> 00:56:11,960
None of them talk to each other.

1616
00:56:11,960 --> 00:56:15,520
You're trying to reassemble a broken picture from disconnected pieces.

1617
00:56:15,520 --> 00:56:17,720
With observability, the picture is already assembled.

1618
00:56:17,720 --> 00:56:20,640
The investigation becomes a simple search instead of an archaeological dig.

1619
00:56:20,640 --> 00:56:22,520
The compliance benefit is just as big.

1620
00:56:22,520 --> 00:56:25,920
When a regulator asks if you're monitoring your agents, you can actually prove it.

1621
00:56:25,920 --> 00:56:29,440
You have the logs to show that governance wasn't just a policy on paper.

1622
00:56:29,440 --> 00:56:30,960
It was enforced in real time.

1623
00:56:30,960 --> 00:56:33,800
But beyond compliance, this enables constant improvement.

1624
00:56:33,800 --> 00:56:37,400
You can finally see which agents are making good decisions and where the gaps are in your

1625
00:56:37,400 --> 00:56:38,400
ontology.

1626
00:56:38,400 --> 00:56:41,120
You can't answer those questions if you can't see the work.

1627
00:56:41,120 --> 00:56:45,120
With data, you optimize based on evidence instead of just hoping for the best.

1628
00:56:45,120 --> 00:56:48,120
The shift is moving from hoping things work to knowing they work.

1629
00:56:48,120 --> 00:56:52,680
It's moving from compliance theatre to actual control.

1630
00:56:52,680 --> 00:56:54,760
The shift in organizational structure.

1631
00:56:54,760 --> 00:56:58,960
Everything we've discussed, identity, reasoning, governance, and observability, those are

1632
00:56:58,960 --> 00:57:00,440
infrastructure layers.

1633
00:57:00,440 --> 00:57:02,360
But infrastructure doesn't exist in a vacuum.

1634
00:57:02,360 --> 00:57:03,960
It changes how organizations work.

1635
00:57:03,960 --> 00:57:05,200
It changes what teams do.

1636
00:57:05,200 --> 00:57:06,960
It changes where the power lives.

1637
00:57:06,960 --> 00:57:10,480
When you don't have a landing zone, AI adoption is a mess.

1638
00:57:10,480 --> 00:57:13,560
Every team that wants to build an agent has to start from scratch.

1639
00:57:13,560 --> 00:57:15,280
They figure out identity on their own.

1640
00:57:15,280 --> 00:57:16,960
They decide how to connect to models.

1641
00:57:16,960 --> 00:57:18,600
They define their own policies.

1642
00:57:18,600 --> 00:57:19,720
Each project is an island.

1643
00:57:19,720 --> 00:57:21,720
Each team spends months reinventing the wheel.

1644
00:57:21,720 --> 00:57:26,040
You end up with fragments scattered across different clouds, using different standards

1645
00:57:26,040 --> 00:57:27,440
and enforcing different rules.

1646
00:57:27,440 --> 00:57:28,960
It might work in isolation.

1647
00:57:28,960 --> 00:57:33,120
But when you look at the company's total AI footprint, it's chaos.

1648
00:57:33,120 --> 00:57:35,560
With a landing zone, that structure inverts.

1649
00:57:35,560 --> 00:57:37,080
There is a central platform team.

1650
00:57:37,080 --> 00:57:38,440
Their job isn't to build the agents.

1651
00:57:38,440 --> 00:57:41,160
It's to build the platform that makes building agents possible.

1652
00:57:41,160 --> 00:57:42,560
They design the identity model.

1653
00:57:42,560 --> 00:57:44,000
They set up the governance hub.

1654
00:57:44,000 --> 00:57:45,520
They maintain the ontologies.

1655
00:57:45,520 --> 00:57:47,560
Everything else is built on top of that foundation.

1656
00:57:47,560 --> 00:57:51,000
This changes what it means to be a project team.

1657
00:57:51,000 --> 00:57:52,120
We have started with a blank slate.

1658
00:57:52,120 --> 00:57:53,960
A team starts with an inheritance.

1659
00:57:53,960 --> 00:57:55,560
The identity layer is ready.

1660
00:57:55,560 --> 00:57:57,800
And the policy framework already exists.

1661
00:57:57,800 --> 00:57:59,400
The team doesn't have to invent the plumbing.

1662
00:57:59,400 --> 00:58:03,800
They focus on what makes their agent unique, like the business logic or the specific workflow.

1663
00:58:03,800 --> 00:58:07,680
They get to market faster because they aren't rebuilding the governance layer every single

1664
00:58:07,680 --> 00:58:08,680
time.

1665
00:58:08,680 --> 00:58:10,560
The real shift is in who makes the decisions.

1666
00:58:10,560 --> 00:58:13,520
Without a landing zone, every team decides things independently.

1667
00:58:13,520 --> 00:58:16,920
One team lets agents call any model while another team stays restricted.

1668
00:58:16,920 --> 00:58:19,920
One team logs everything and the next logs nothing at all.

1669
00:58:19,920 --> 00:58:23,800
You get inconsistency because there's nobody there to enforce a standard.

1670
00:58:23,800 --> 00:58:26,040
With a landing zone, the big decisions move up.

1671
00:58:26,040 --> 00:58:31,200
The organization decides on identity standards, approved models and cost controls.

1672
00:58:31,200 --> 00:58:33,480
These become central rules, not suggestions.

1673
00:58:33,480 --> 00:58:34,480
But here's the thing.

1674
00:58:34,480 --> 00:58:36,160
This doesn't slow teams down.

1675
00:58:36,160 --> 00:58:39,760
It actually speeds them up because they aren't wasting time debating the fundamentals.

1676
00:58:39,760 --> 00:58:42,840
They can focus entirely on solving their actual business problem.

1677
00:58:42,840 --> 00:58:45,480
The structure that emerges is a hub and spoke model.

1678
00:58:45,480 --> 00:58:47,320
The central platform team is the hub.

1679
00:58:47,320 --> 00:58:50,600
They provide the shared infrastructure and enforce the baseline standards.

1680
00:58:50,600 --> 00:58:52,040
The business units are the spokes.

1681
00:58:52,040 --> 00:58:54,760
They use that infrastructure to build specific use cases.

1682
00:58:54,760 --> 00:58:58,520
There is finally a clear boundary between what is centralized and what is distributed.

1683
00:58:58,520 --> 00:59:01,720
This solves the coordination problem that kills most companies as they scale.

1684
00:59:01,720 --> 00:59:06,160
Without this model, coordination costs explode as you add more agents.

1685
00:59:06,160 --> 00:59:09,960
Policy discussions never end because there are no actual policies to follow.

1686
00:59:09,960 --> 00:59:13,280
Incidents become nightmares because you are trying to fix incompatible systems.

1687
00:59:13,280 --> 00:59:16,600
The whole organization grinds to a halt because everyone is working against different

1688
00:59:16,600 --> 00:59:17,760
infrastructure.

1689
00:59:17,760 --> 00:59:19,640
With a landing zone, you coordinate once.

1690
00:59:19,640 --> 00:59:22,960
The platform team designs the system and the spokes operate within it.

1691
00:59:22,960 --> 00:59:26,440
New teams onboard faster because they are using proven patterns.

1692
00:59:26,440 --> 00:59:30,040
Policy changes hit the entire company at once because there is one place to flip the switch.

1693
00:59:30,040 --> 00:59:32,280
The time to value metric changes completely.

1694
00:59:32,280 --> 00:59:35,640
A team without a platform might take three months to build an agent spending two of those

1695
00:59:35,640 --> 00:59:37,400
months just on the infrastructure.

1696
00:59:37,400 --> 00:59:39,480
A team with a platform can do it in two weeks.

1697
00:59:39,480 --> 00:59:41,160
That isn't a small improvement.

1698
00:59:41,160 --> 00:59:43,680
That is the difference between an experiment and a deployment.

1699
00:59:43,680 --> 00:59:46,440
It's the difference between a proof of concept and actual production.

1700
00:59:46,440 --> 00:59:51,400
This shift is what transforms AI from a technical toy into a real operational advantage.

1701
00:59:51,400 --> 00:59:53,640
You are no longer managing a bunch of isolated projects.

1702
00:59:53,640 --> 00:59:56,760
You are operating a platform, security and threat detection.

1703
00:59:56,760 --> 01:00:00,880
The moment you decentralize governance is the moment your security perimeter collapses.

1704
01:00:00,880 --> 01:00:01,880
That is the hard truth.

1705
01:00:01,880 --> 01:00:05,080
Most organizations do not grasp until something goes wrong.

1706
01:00:05,080 --> 01:00:06,840
Consider what happens without a landing zone.

1707
01:00:06,840 --> 01:00:10,880
You have an agent built by a contractor that is running somewhere in your environment.

1708
01:00:10,880 --> 01:00:15,080
It has access to a specific set of systems that access was granted months ago.

1709
01:00:15,080 --> 01:00:16,440
Security has reviewed it since.

1710
01:00:16,440 --> 01:00:19,520
The contractor relationship ended but the agent is still running.

1711
01:00:19,520 --> 01:00:21,400
One day that agent gets compromised.

1712
01:00:21,400 --> 01:00:26,080
Maybe the person who built it handed over credentials or perhaps a vulnerability in the code was exploited.

1713
01:00:26,080 --> 01:00:28,440
The point is, control is lost.

1714
01:00:28,440 --> 01:00:31,120
Now that compromised agent can move laterally, it has credentials.

1715
01:00:31,120 --> 01:00:32,520
It knows how to call your APIs.

1716
01:00:32,520 --> 01:00:33,720
It can access your data.

1717
01:00:33,720 --> 01:00:35,040
But here is the problem.

1718
01:00:35,040 --> 01:00:38,680
Because the agent was built independently, it was never wired into your threat detection

1719
01:00:38,680 --> 01:00:39,680
systems.

1720
01:00:39,680 --> 01:00:40,920
Defender does not know it exists.

1721
01:00:40,920 --> 01:00:45,240
Your security team has no baseline for what normal behavior looks like for this specific tool.

1722
01:00:45,240 --> 01:00:46,480
So it starts accessing data.

1723
01:00:46,480 --> 01:00:47,640
It should not touch.

1724
01:00:47,640 --> 01:00:49,520
It starts making calls to external systems.

1725
01:00:49,520 --> 01:00:51,720
It starts exfiltrating information.

1726
01:00:51,720 --> 01:00:55,200
And you do not detect it until weeks later when the damage is already done.

1727
01:00:55,200 --> 01:00:59,560
You discover it not because your system is courted, but because a customer reports a problem

1728
01:00:59,560 --> 01:01:02,720
or an auditor finds suspicious patterns in the logs.

1729
01:01:02,720 --> 01:01:06,120
This is a breach that happens in slow motion while you are looking somewhere else.

1730
01:01:06,120 --> 01:01:07,800
With a landing zone, the story changes.

1731
01:01:07,800 --> 01:01:12,920
That same agent exists, but it is integrated into your security infrastructure from day one.

1732
01:01:12,920 --> 01:01:14,600
Defender monitors it constantly.

1733
01:01:14,600 --> 01:01:19,520
Every API call the agent makes gets logged and evaluated against behavioral baselines.

1734
01:01:19,520 --> 01:01:24,160
Defender understands that this agent normally calls the revenue API and the customer database.

1735
01:01:24,160 --> 01:01:28,280
Those are normal patterns, but today it is trying to call the financial export service

1736
01:01:28,280 --> 01:01:30,480
and the employee records database.

1737
01:01:30,480 --> 01:01:32,000
Those are outside the baseline.

1738
01:01:32,000 --> 01:01:33,000
And normally detected.

1739
01:01:33,000 --> 01:01:34,640
The request gets blocked immediately.

1740
01:01:34,640 --> 01:01:36,320
The security team gets an alert.

1741
01:01:36,320 --> 01:01:38,760
They can investigate before any real damage occurs.

1742
01:01:38,760 --> 01:01:40,480
This is defense in depth in practice.

1743
01:01:40,480 --> 01:01:42,760
You do not rely on one single control.

1744
01:01:42,760 --> 01:01:43,760
Identity is the first layer.

1745
01:01:43,760 --> 01:01:48,240
The agent has its own identity, so you know it is the agent and not someone else using

1746
01:01:48,240 --> 01:01:49,240
its account.

1747
01:01:49,240 --> 01:01:52,840
Policy enforcement is the second layer, even if the agent has an identity, it can only

1748
01:01:52,840 --> 01:01:56,120
call approved APIs based on policies you have defined.

1749
01:01:56,120 --> 01:01:57,960
Threat detection is the third layer.

1750
01:01:57,960 --> 01:02:01,640
Even if the agent is allowed to call certain APIs if it calls them in abnormal ways, the

1751
01:02:01,640 --> 01:02:02,800
system notices.

1752
01:02:02,800 --> 01:02:06,400
Other trails are the fourth layer, even if something slips through, you have a complete record

1753
01:02:06,400 --> 01:02:08,040
of exactly what happened.

1754
01:02:08,040 --> 01:02:11,000
Most organizations build security as an afterthought.

1755
01:02:11,000 --> 01:02:13,640
You build the agent first, get it working and ship it.

1756
01:02:13,640 --> 01:02:17,480
Then you add monitoring, then you add policies, then you try to integrate it with your security

1757
01:02:17,480 --> 01:02:18,480
stack.

1758
01:02:18,480 --> 01:02:21,360
By that point, the agent has already been operating unsupervised.

1759
01:02:21,360 --> 01:02:24,880
It is already running, without proper oversight, you are playing catch up.

1760
01:02:24,880 --> 01:02:27,280
A landing zone inverts that model.

1761
01:02:27,280 --> 01:02:28,280
Security is not added.

1762
01:02:28,280 --> 01:02:29,280
It is built in.

1763
01:02:29,280 --> 01:02:30,480
An agent cannot run without an identity.

1764
01:02:30,480 --> 01:02:32,280
The identity comes with default policies.

1765
01:02:32,280 --> 01:02:35,080
These policies are evaluated by the gateway in real time.

1766
01:02:35,080 --> 01:02:36,680
That evaluation gets logged.

1767
01:02:36,680 --> 01:02:40,200
Threat detection systems monitor the agent behavior based on patterns you have defined.

1768
01:02:40,200 --> 01:02:41,720
The agent is not a special case.

1769
01:02:41,720 --> 01:02:45,840
It is a managed entity with the same level of security scrutiny as any user or application.

1770
01:02:45,840 --> 01:02:47,520
The operational shift is profound.

1771
01:02:47,520 --> 01:02:49,520
Without integration, security is reactive.

1772
01:02:49,520 --> 01:02:50,520
Something goes wrong.

1773
01:02:50,520 --> 01:02:51,520
You investigate.

1774
01:02:51,520 --> 01:02:52,720
You respond.

1775
01:02:52,720 --> 01:02:55,920
Organizations spend months recovering from these breaches.

1776
01:02:55,920 --> 01:02:58,240
With a landing zone security is proactive.

1777
01:02:58,240 --> 01:03:00,440
You detect anomalies before they become breaches.

1778
01:03:00,440 --> 01:03:04,400
You contain threats immediately because the infrastructure is already built to do it.

1779
01:03:04,400 --> 01:03:06,920
This changes the conversation with your security team.

1780
01:03:06,920 --> 01:03:10,360
Instead of arguing about whether agents should be monitored, you are discussing how they

1781
01:03:10,360 --> 01:03:11,560
should be monitored.

1782
01:03:11,560 --> 01:03:14,200
The infrastructure already assumes threats will be attempted.

1783
01:03:14,200 --> 01:03:17,440
Your job is to make sure they are detected and contained.

1784
01:03:17,440 --> 01:03:19,080
The transition strategy.

1785
01:03:19,080 --> 01:03:23,560
Everything we have discussed is conceptual until you actually move from one model to another.

1786
01:03:23,560 --> 01:03:25,480
And this is where organizations stumble.

1787
01:03:25,480 --> 01:03:27,560
Because the temptation is to do it all at once.

1788
01:03:27,560 --> 01:03:31,560
You can't even think about the old system.

1789
01:03:31,560 --> 01:03:35,360
The jump from co-pilot as a feature to agent mash as infrastructure requires deliberate

1790
01:03:35,360 --> 01:03:36,360
steps.

1791
01:03:36,360 --> 01:03:37,960
The sequence matters more than the speed.

1792
01:03:37,960 --> 01:03:41,560
Start with inventory, not design, not architecture and not planning.

1793
01:03:41,560 --> 01:03:42,560
Inventory.

1794
01:03:42,560 --> 01:03:43,880
You need to know what you actually have.

1795
01:03:43,880 --> 01:03:45,160
How many agents are running?

1796
01:03:45,160 --> 01:03:46,160
Where are they running?

1797
01:03:46,160 --> 01:03:47,160
What are they doing?

1798
01:03:47,160 --> 01:03:48,160
Who built them?

1799
01:03:48,160 --> 01:03:49,160
Who maintains them?

1800
01:03:49,160 --> 01:03:50,160
This is the discovery phase.

1801
01:03:50,160 --> 01:03:53,560
And it is uncomfortable because you will find things you did not know existed.

1802
01:03:53,560 --> 01:03:56,880
You will find agents running in development that were promoted to production two years

1803
01:03:56,880 --> 01:03:57,880
ago and forgotten.

1804
01:03:57,880 --> 01:04:02,120
You will find agents built by contractors that are still calling external APIs.

1805
01:04:02,120 --> 01:04:05,440
You will find agents that were supposed to be retired but keep running because nobody

1806
01:04:05,440 --> 01:04:06,440
pulled the plug.

1807
01:04:06,440 --> 01:04:08,200
You will find chaos.

1808
01:04:08,200 --> 01:04:11,960
But chaos that you can see is better than chaos you do not know about.

1809
01:04:11,960 --> 01:04:12,960
Document what you find.

1810
01:04:12,960 --> 01:04:18,200
Agent name, owner, location, access level, data sources and model dependencies.

1811
01:04:18,200 --> 01:04:20,280
You are building an inventory, not a solution yet.

1812
01:04:20,280 --> 01:04:22,320
This phase takes weeks, not months.

1813
01:04:22,320 --> 01:04:24,160
You are not fixing anything.

1814
01:04:24,160 --> 01:04:25,960
You are just seeing what is there.

1815
01:04:25,960 --> 01:04:28,320
Once you have visibility, design the landing zone.

1816
01:04:28,320 --> 01:04:30,560
This is where you define the target architecture.

1817
01:04:30,560 --> 01:04:31,560
Not all of it.

1818
01:04:31,560 --> 01:04:33,640
Just the version you are going to build first.

1819
01:04:33,640 --> 01:04:34,920
Decide on your identity model.

1820
01:04:34,920 --> 01:04:36,720
Will it be an agent ID exclusively?

1821
01:04:36,720 --> 01:04:38,960
Will you support hybrid models during the transition?

1822
01:04:38,960 --> 01:04:42,200
How will you handle legacy agents that cannot migrate immediately?

1823
01:04:42,200 --> 01:04:43,720
Design your governance hub.

1824
01:04:43,720 --> 01:04:45,120
What models will be approved?

1825
01:04:45,120 --> 01:04:46,120
What tools?

1826
01:04:46,120 --> 01:04:47,120
What policies?

1827
01:04:47,120 --> 01:04:48,120
Design your data plane.

1828
01:04:48,120 --> 01:04:49,680
Which ontologies will you build?

1829
01:04:49,680 --> 01:04:51,880
Which existing data sources will you expose?

1830
01:04:51,880 --> 01:04:54,360
What migration path makes sense for your data?

1831
01:04:54,360 --> 01:04:55,880
Design your compliance posture.

1832
01:04:55,880 --> 01:04:57,360
What regulations matter?

1833
01:04:57,360 --> 01:05:00,360
What policies need to be enforced at the infrastructure level?

1834
01:05:00,360 --> 01:05:01,720
Design your cost model.

1835
01:05:01,720 --> 01:05:03,160
How will you attribute spend?

1836
01:05:03,160 --> 01:05:04,480
What budgets will you set?

1837
01:05:04,480 --> 01:05:05,680
This is not construction.

1838
01:05:05,680 --> 01:05:07,000
It is specification.

1839
01:05:07,000 --> 01:05:11,200
You are defining the target state clearly, so teams know what they are moving toward.

1840
01:05:11,200 --> 01:05:12,520
This phase takes weeks.

1841
01:05:12,520 --> 01:05:14,120
It is collaborative.

1842
01:05:14,120 --> 01:05:18,080
Security compliance, IT and business teams should all have input because they will all be

1843
01:05:18,080 --> 01:05:19,080
affected.

1844
01:05:19,080 --> 01:05:21,320
Migration happens in phases, not all at once.

1845
01:05:21,320 --> 01:05:22,680
And the order matters.

1846
01:05:22,680 --> 01:05:24,200
Start with high risk agents.

1847
01:05:24,200 --> 01:05:28,000
The ones accessing sensitive data, the ones in regulated industries, the ones that are

1848
01:05:28,000 --> 01:05:32,280
business critical, not because they are easiest, but because they are most important.

1849
01:05:32,280 --> 01:05:33,600
These are your proof points.

1850
01:05:33,600 --> 01:05:35,120
This is where governance matters most.

1851
01:05:35,120 --> 01:05:40,040
Migrate a small batch, prove the pattern works, document the process, and then scale.

1852
01:05:40,040 --> 01:05:42,760
Real organizations do not move every agent simultaneously.

1853
01:05:42,760 --> 01:05:44,080
They move in waves.

1854
01:05:44,080 --> 01:05:46,160
Month one, critical financial agents.

1855
01:05:46,160 --> 01:05:48,880
Month two, compliance related agents.

1856
01:05:48,880 --> 01:05:50,320
Month three.

1857
01:05:50,320 --> 01:05:51,920
Customer facing agents.

1858
01:05:51,920 --> 01:05:53,360
Month four.

1859
01:05:53,360 --> 01:05:55,120
Personal operations agents.

1860
01:05:55,120 --> 01:05:56,440
Month five.

1861
01:05:56,440 --> 01:05:58,280
Experimental and lower risk agents.

1862
01:05:58,280 --> 01:05:59,280
Month six.

1863
01:05:59,280 --> 01:06:00,720
Clean up and sunset of legacy systems.

1864
01:06:00,720 --> 01:06:01,720
This is not slow.

1865
01:06:01,720 --> 01:06:02,720
It is methodical.

1866
01:06:02,720 --> 01:06:06,280
And it is essential because every batch of migrations teaches you something.

1867
01:06:06,280 --> 01:06:08,640
You will find problems you did not anticipate.

1868
01:06:08,640 --> 01:06:11,520
You will discover policy configurations that need adjustment.

1869
01:06:11,520 --> 01:06:13,280
You will identify tooling gaps.

1870
01:06:13,280 --> 01:06:15,960
You can only learn this at scale, not in pilots.

1871
01:06:15,960 --> 01:06:17,080
Establish governance as you move.

1872
01:06:17,080 --> 01:06:18,080
Not after.

1873
01:06:18,080 --> 01:06:19,600
This is the principle most organizations.

1874
01:06:19,600 --> 01:06:20,600
Missed.

1875
01:06:20,600 --> 01:06:21,800
They migrate the agents first and the governance later.

1876
01:06:21,800 --> 01:06:22,800
That is backward.

1877
01:06:22,800 --> 01:06:24,720
governance first, migration second.

1878
01:06:24,720 --> 01:06:27,600
Before an agent moves to the new system, it gets an identity.

1879
01:06:27,600 --> 01:06:28,920
It gets enrolled in policies.

1880
01:06:28,920 --> 01:06:30,560
It gets connected to observability.

1881
01:06:30,560 --> 01:06:32,160
It gets its life cycle managed.

1882
01:06:32,160 --> 01:06:33,880
That agent is not free floating.

1883
01:06:33,880 --> 01:06:36,920
From day one, it is operating within the constraints you have defined.

1884
01:06:36,920 --> 01:06:41,080
Yes, this slows down the initial migration, but it prevents the chaos of moving infrastructure

1885
01:06:41,080 --> 01:06:42,080
without control.

1886
01:06:42,080 --> 01:06:45,160
By the time you finish migration governance is already embedded.

1887
01:06:45,160 --> 01:06:46,960
You are not bolting it on afterward.

1888
01:06:46,960 --> 01:06:48,480
Here is what a real timeline looks like.

1889
01:06:48,480 --> 01:06:52,040
A large organization with 300 agents needs about six months.

1890
01:06:52,040 --> 01:06:54,320
One and two are inventory and design.

1891
01:06:54,320 --> 01:06:56,560
Months three through five are phased migration.

1892
01:06:56,560 --> 01:06:58,280
Month six is clean up in stabilization.

1893
01:06:58,280 --> 01:07:00,080
At the end, you have an agent mesh.

1894
01:07:00,080 --> 01:07:01,840
All agents have traceable identities.

1895
01:07:01,840 --> 01:07:03,760
All policies are enforced consistently.

1896
01:07:03,760 --> 01:07:05,400
All ontologies are shared.

1897
01:07:05,400 --> 01:07:06,720
All costs are visible.

1898
01:07:06,720 --> 01:07:09,400
All compliance is structural, not just documented.

1899
01:07:09,400 --> 01:07:13,720
The key principle underneath all of this is simple, but counter-intuitive.

1900
01:07:13,720 --> 01:07:15,480
Governance comes before innovation.

1901
01:07:15,480 --> 01:07:18,480
Not as a slogan or a policy document, but structurally.

1902
01:07:18,480 --> 01:07:20,160
You build the constraints first.

1903
01:07:20,160 --> 01:07:24,840
When teams innovate within those constraints, it feels restrictive, but it is actually liberating.

1904
01:07:24,840 --> 01:07:29,040
Because once the foundation is set, teams move fast knowing their innovation won't create

1905
01:07:29,040 --> 01:07:31,760
chaos downstream in the future.

1906
01:07:31,760 --> 01:07:32,920
Autonomous operations.

1907
01:07:32,920 --> 01:07:37,120
Once you have the foundation in place, identity, reasoning, governance and observability,

1908
01:07:37,120 --> 01:07:39,240
the math of what's possible changes.

1909
01:07:39,240 --> 01:07:41,120
Not in a theoretical way, in an operational way.

1910
01:07:41,120 --> 01:07:44,120
Imagine an operations agent in a manufacturing plant.

1911
01:07:44,120 --> 01:07:46,400
It's monitoring production KPIs in real time.

1912
01:07:46,400 --> 01:07:51,200
It understands the ontology that defines what normal looks like for this specific business.

1913
01:07:51,200 --> 01:07:54,000
Line 3 should be hitting 500 units every hour.

1914
01:07:54,000 --> 01:07:56,560
Right now it's at 4.20, that's below the threshold.

1915
01:07:56,560 --> 01:07:58,920
It isn't a catastrophe yet, but it's a deviation.

1916
01:07:58,920 --> 01:08:00,680
The agent's instructions are clear.

1917
01:08:00,680 --> 01:08:03,120
Flag the minor issues and escalate the major ones.

1918
01:08:03,120 --> 01:08:04,520
First it checks the context.

1919
01:08:04,520 --> 01:08:05,520
Is today a training day?

1920
01:08:05,520 --> 01:08:06,520
No.

1921
01:08:06,520 --> 01:08:07,520
Is there maintenance scheduled?

1922
01:08:07,520 --> 01:08:09,000
No.

1923
01:08:09,000 --> 01:08:12,720
Because the ontology defines the relationships between equipment and output, the agent can

1924
01:08:12,720 --> 01:08:14,840
traverse those parts to find a root cause.

1925
01:08:14,840 --> 01:08:19,160
It sees a 40% correlation between this specific slowdown and equipment degradation.

1926
01:08:19,160 --> 01:08:21,160
It pulls the recent maintenance records.

1927
01:08:21,160 --> 01:08:22,680
Everything looked fine during the last service.

1928
01:08:22,680 --> 01:08:24,360
It checks the raw material quality.

1929
01:08:24,360 --> 01:08:25,360
Those metrics are normal.

1930
01:08:25,360 --> 01:08:26,520
It looks at labor data.

1931
01:08:26,520 --> 01:08:27,520
No absences.

1932
01:08:27,520 --> 01:08:29,080
No schedule shifts.

1933
01:08:29,080 --> 01:08:32,920
Because the ontology constrains the agent to only relevant data, it can surface a specific

1934
01:08:32,920 --> 01:08:33,920
hypothesis.

1935
01:08:33,920 --> 01:08:36,520
There is a minor calibration drift on the material feeder.

1936
01:08:36,520 --> 01:08:38,960
But here's where autonomy actually starts to mean something.

1937
01:08:38,960 --> 01:08:41,160
The agent doesn't just send a report and wait.

1938
01:08:41,160 --> 01:08:45,080
It's authorized to act.

1939
01:08:45,080 --> 01:08:46,520
A calibration check.

1940
01:08:46,520 --> 01:08:48,800
That request doesn't just fire off into the void.

1941
01:08:48,800 --> 01:08:50,080
It flows through policy gates.

1942
01:08:50,080 --> 01:08:52,040
The system checks the agent's identity.

1943
01:08:52,040 --> 01:08:53,960
Does it have permission to request maintenance?

1944
01:08:53,960 --> 01:08:54,960
Yes.

1945
01:08:54,960 --> 01:08:55,960
Is it targeting an approved system?

1946
01:08:55,960 --> 01:08:56,960
Yes.

1947
01:08:56,960 --> 01:08:57,960
Is there sensitive data involved?

1948
01:08:57,960 --> 01:08:58,960
No.

1949
01:08:58,960 --> 01:08:59,960
The request passes policy.

1950
01:08:59,960 --> 01:09:00,960
It hits the maintenance system.

1951
01:09:00,960 --> 01:09:04,040
A technician reviews the reasoning sees the data and hits a prove.

1952
01:09:04,040 --> 01:09:05,560
The calibration happens.

1953
01:09:05,560 --> 01:09:08,480
Production climbs back up to 495 units per hour.

1954
01:09:08,480 --> 01:09:09,880
The problem is solved.

1955
01:09:09,880 --> 01:09:11,960
But here is the shift that isn't automation.

1956
01:09:11,960 --> 01:09:13,400
That's orchestration.

1957
01:09:13,400 --> 01:09:16,680
The agent didn't just decide things in a vacuum, but it also didn't sit around waiting for

1958
01:09:16,680 --> 01:09:18,520
a human to tell it where to look.

1959
01:09:18,520 --> 01:09:21,560
It reasoned through the problem space that the ontology provided.

1960
01:09:21,560 --> 01:09:23,640
It made a recommendation based on that logic.

1961
01:09:23,640 --> 01:09:26,280
It initiated an action within its authorized boundaries.

1962
01:09:26,280 --> 01:09:27,920
The human made the final call.

1963
01:09:27,920 --> 01:09:29,880
But the agent did the cognitive heavy lifting.

1964
01:09:29,880 --> 01:09:33,880
That is the efficiency you get when identity reasoning and governance finally align.

1965
01:09:33,880 --> 01:09:35,280
Now scale that.

1966
01:09:35,280 --> 01:09:37,680
Imagine 10 agents across 10 different facilities.

1967
01:09:37,680 --> 01:09:39,200
They monitor different KPIs.

1968
01:09:39,200 --> 01:09:42,920
The reason over the same ontologies, they follow the same policies they are autonomous within

1969
01:09:42,920 --> 01:09:43,920
their boundaries.

1970
01:09:43,920 --> 01:09:46,440
You don't need a central command center barking instruction.

1971
01:09:46,440 --> 01:09:48,920
You don't need a human reviewer for every tiny move.

1972
01:09:48,920 --> 01:09:50,000
The agents operate.

1973
01:09:50,000 --> 01:09:51,000
They detect.

1974
01:09:51,000 --> 01:09:52,000
They propose.

1975
01:09:52,000 --> 01:09:53,680
They act.

1976
01:09:53,680 --> 01:09:54,800
Humans oversee the system.

1977
01:09:54,800 --> 01:09:56,000
Not the individual decisions.

1978
01:09:56,000 --> 01:09:57,520
It's a different model of work.

1979
01:09:57,520 --> 01:09:59,720
And here is why this doesn't turn into chaos.

1980
01:09:59,720 --> 01:10:00,960
Everything is auditable.

1981
01:10:00,960 --> 01:10:02,280
Everything is reversible.

1982
01:10:02,280 --> 01:10:05,320
Every single move an agent makes is logged with full context.

1983
01:10:05,320 --> 01:10:06,680
You know who authorized it?

1984
01:10:06,680 --> 01:10:08,120
You see the reasoning that led to it.

1985
01:10:08,120 --> 01:10:09,120
You see the outcome.

1986
01:10:09,120 --> 01:10:11,400
When an agent makes a mistake, you undo the action.

1987
01:10:11,400 --> 01:10:14,840
If it oversteeps the boundary, the logs show you exactly how it happened.

1988
01:10:14,840 --> 01:10:17,080
The system isn't trusting these agents blindly.

1989
01:10:17,080 --> 01:10:20,120
It's trusting them within transparent, hard-coded constraints.

1990
01:10:20,120 --> 01:10:22,160
This also makes coordination possible.

1991
01:10:22,160 --> 01:10:25,800
When agents use the same ontologies and policies, they can coordinate without being told

1992
01:10:25,800 --> 01:10:26,800
to.

1993
01:10:26,800 --> 01:10:30,440
One agent finds a supply issue and updates the shared context about material levels.

1994
01:10:30,440 --> 01:10:34,760
Another agent on the production side sees that update and adjusts its recommendations automatically.

1995
01:10:34,760 --> 01:10:36,280
They aren't talking to each other.

1996
01:10:36,280 --> 01:10:39,280
They're both reading from and writing to the same business model.

1997
01:10:39,280 --> 01:10:41,680
That's how an intelligent organization actually works.

1998
01:10:41,680 --> 01:10:45,200
Not through top-down commands, but through a shared understanding of what is true.

1999
01:10:45,200 --> 01:10:46,520
The human role doesn't go away.

2000
01:10:46,520 --> 01:10:47,520
It just moves.

2001
01:10:47,520 --> 01:10:51,000
Instead of making every decision or checking every box, humans set the direction.

2002
01:10:51,000 --> 01:10:52,080
They write the policies.

2003
01:10:52,080 --> 01:10:53,080
They build the boundaries.

2004
01:10:53,080 --> 01:10:55,360
They monitor the health of the whole machine.

2005
01:10:55,360 --> 01:10:58,840
They step in when a decision requires judgment or human values.

2006
01:10:58,840 --> 01:11:00,280
The agents handle the execution.

2007
01:11:00,280 --> 01:11:01,280
They handle the speed.

2008
01:11:01,280 --> 01:11:02,920
They handle the data at scale.

2009
01:11:02,920 --> 01:11:05,480
This is what autonomous operations look like in practice.

2010
01:11:05,480 --> 01:11:07,120
It isn't agents running wild.

2011
01:11:07,120 --> 01:11:12,040
It's agents operating with total clarity inside a cage of constraints, guided by an ontology

2012
01:11:12,040 --> 01:11:14,200
that actually understands your business.

2013
01:11:14,200 --> 01:11:17,680
It's humans keeping oversight, while the system scales far beyond what manual management

2014
01:11:17,680 --> 01:11:19,320
could ever touch.

2015
01:11:19,320 --> 01:11:20,640
Copilot is the gateway drug.

2016
01:11:20,640 --> 01:11:21,640
It works.

2017
01:11:21,640 --> 01:11:22,640
It feels good.

2018
01:11:22,640 --> 01:11:23,640
It makes you feel productive.

2019
01:11:23,640 --> 01:11:24,960
But Copilot isn't the platform.

2020
01:11:24,960 --> 01:11:27,120
It's just a feature that proves you need a platform.

2021
01:11:27,120 --> 01:11:30,800
The real shift moving from a chat box to an agent fabric requires infrastructure.

2022
01:11:30,800 --> 01:11:32,000
You need identity.

2023
01:11:32,000 --> 01:11:33,320
So agents are accountable.

2024
01:11:33,320 --> 01:11:34,320
You need ontologies.

2025
01:11:34,320 --> 01:11:35,520
You need reason correctly.

2026
01:11:35,520 --> 01:11:37,960
You need governance hubs, so policies stay consistent.

2027
01:11:37,960 --> 01:11:41,160
You need landing zones, so the system scales without breaking.

2028
01:11:41,160 --> 01:11:44,880
The organizations that build this foundation first will treat AI as a managed, auditable

2029
01:11:44,880 --> 01:11:45,880
capability.

2030
01:11:45,880 --> 01:11:48,560
The ones that don't, they're just building governance debt.

2031
01:11:48,560 --> 01:11:50,960
They'll spend the next decade trying to pay it off.

2032
01:11:50,960 --> 01:11:54,360
The future belongs to the companies that treat AI infrastructure like any other critical

2033
01:11:54,360 --> 01:11:56,360
system designed on purpose.

2034
01:11:56,360 --> 01:11:57,360
Built methodically.

2035
01:11:57,360 --> 01:11:58,360
Gov.

2036
01:11:58,360 --> 01:11:59,360
Every single day.

2037
01:11:59,360 --> 01:12:00,360
That's what the agent measures.

2038
01:12:00,360 --> 01:12:01,360
It isn't a technology choice.

2039
01:12:01,360 --> 01:12:05,240
the structural decision about how you're going to operate at scale.