1 00:00:00,000 --> 00:00:01,520 What exactly is Azure AI Foundry? 2 00:00:01,520 --> 00:00:04,640 Is it just another service Microsoft added or is it something bigger? 3 00:00:04,640 --> 00:00:08,600 Over the past year, Microsoft has been making AI announcements left and right. 4 00:00:08,600 --> 00:00:09,760 It's easy to feel lost. 5 00:00:09,760 --> 00:00:11,800 I'm Mercospitus from Microsoft Knowledge Nuggets. 6 00:00:11,800 --> 00:00:15,520 By the end of this episode, you'll know what Foundry actually is and why it matters. 7 00:00:15,520 --> 00:00:16,720 No marketing fluff. 8 00:00:16,720 --> 00:00:20,920 We'll break it down into clear parts, what it is, what's inside, and how it all connects. 9 00:00:20,920 --> 00:00:23,000 Why everyone is confused about the name? 10 00:00:23,000 --> 00:00:24,560 Confusion starts with the name. 11 00:00:24,560 --> 00:00:27,160 Foundry has gone through more name changes than I can count. 12 00:00:27,160 --> 00:00:32,480 First, it was cognitive services, then Azure AI Services, then Azure AI Studio, then Azure AI Foundry. 13 00:00:32,480 --> 00:00:35,440 And as of late last year, it's officially Microsoft Foundry. 14 00:00:35,440 --> 00:00:37,600 Same product four or five names in about three years. 15 00:00:37,600 --> 00:00:40,840 That alone is confusing, but it gets more complicated right now. 16 00:00:40,840 --> 00:00:43,240 Two versions of the platform run side by side. 17 00:00:43,240 --> 00:00:46,000 The classic hub-based architecture is the old way. 18 00:00:46,000 --> 00:00:48,120 The new Foundry project model is the modern version. 19 00:00:48,120 --> 00:00:51,920 Both live at the same URL, AIazure.com. 20 00:00:51,920 --> 00:00:55,960 You go there and find a toggle button that switches between two completely different architectures. 21 00:00:55,960 --> 00:00:57,240 It's not just a new look. 22 00:00:57,240 --> 00:00:59,640 It's a different way of organizing everything underneath. 23 00:00:59,640 --> 00:01:03,920 This confuses beginners because when you search for help, you find articles about hubs and 24 00:01:03,920 --> 00:01:04,920 classic projects. 25 00:01:04,920 --> 00:01:06,360 Those articles are still accurate. 26 00:01:06,360 --> 00:01:08,040 Microsoft supports both versions. 27 00:01:08,040 --> 00:01:12,640 But the new features, agent capabilities, and everything Microsoft is investing in only 28 00:01:12,640 --> 00:01:13,920 come to Foundry projects. 29 00:01:13,920 --> 00:01:17,520 You could follow an old tutorial, set up a hub, and then discover you can't use the latest 30 00:01:17,520 --> 00:01:18,520 tools. 31 00:01:18,520 --> 00:01:20,320 That's a real headache, so here's my advice. 32 00:01:20,320 --> 00:01:21,320 Ignore the old stuff. 33 00:01:21,320 --> 00:01:23,760 Modern Foundry is where Microsoft is putting everything. 34 00:01:23,760 --> 00:01:27,640 If you're starting fresh today, skip the hubs and go straight to Foundry projects. 35 00:01:27,640 --> 00:01:31,760 That's where the new capabilities live, so what Azure AI Foundry actually is. 36 00:01:31,760 --> 00:01:32,800 Let's start from scratch. 37 00:01:32,800 --> 00:01:34,400 What is Azure AI Foundry? 38 00:01:34,400 --> 00:01:38,520 It's a unified platform for building, testing, and deploying AI applications all in one 39 00:01:38,520 --> 00:01:39,520 place. 40 00:01:39,520 --> 00:01:40,520 That's the simplest definition. 41 00:01:40,520 --> 00:01:41,880 Think of it like a factory. 42 00:01:41,880 --> 00:01:43,280 Raw materials come in. 43 00:01:43,280 --> 00:01:45,760 Models, data, tools. 44 00:01:45,760 --> 00:01:46,960 Finished applications come out. 45 00:01:46,960 --> 00:01:49,080 Chatbots, co-pilots, automation agents. 46 00:01:49,080 --> 00:01:50,600 You don't build the factory yourself. 47 00:01:50,600 --> 00:01:51,600 Microsoft runs it. 48 00:01:51,600 --> 00:01:53,920 You just walk in, grab what you need and start building. 49 00:01:53,920 --> 00:01:56,720 This isn't a single product like a word or Excel. 50 00:01:56,720 --> 00:02:00,680 Foundry is a container that holds models, tools, projects, and security. 51 00:02:00,680 --> 00:02:03,800 Create one Foundry resource and you get the entire model catalog. 52 00:02:03,800 --> 00:02:04,800 Thousands of models. 53 00:02:04,800 --> 00:02:08,720 You get agent building tools, testing playgrounds, evaluation tools, and security, all bundled 54 00:02:08,720 --> 00:02:09,720 together. 55 00:02:09,720 --> 00:02:11,520 Now here's a key distinction. 56 00:02:11,520 --> 00:02:12,520 Foundry is for developers. 57 00:02:12,520 --> 00:02:17,040 If you write Python or CYs or I'd, if you want to build custom AI solutions with code, 58 00:02:17,040 --> 00:02:18,320 this is your platform. 59 00:02:18,320 --> 00:02:21,040 Looking for a low code, drag, and drop experience. 60 00:02:21,040 --> 00:02:24,600 Co-pilot Studio, a different tool for a different audience. 61 00:02:24,600 --> 00:02:26,800 Both are valid. 62 00:02:26,800 --> 00:02:31,440 Before Foundry building an AI application on Azure meant provisioning separate services. 63 00:02:31,440 --> 00:02:35,900 You'd set up Azure OpenAI, cognitive services, Azure Machine Learning, Azure AI Search, 64 00:02:35,900 --> 00:02:37,880 then write custom code to stitch them together. 65 00:02:37,880 --> 00:02:38,880 A lot of work. 66 00:02:38,880 --> 00:02:39,880 Foundry replaces all that. 67 00:02:39,880 --> 00:02:42,480 Create one resource and everything is already connected. 68 00:02:42,480 --> 00:02:43,480 Models are there. 69 00:02:43,480 --> 00:02:44,480 Tools are there. 70 00:02:44,480 --> 00:02:45,480 Security is built in. 71 00:02:45,480 --> 00:02:46,480 You just start building. 72 00:02:46,480 --> 00:02:47,480 That's the point. 73 00:02:47,480 --> 00:02:48,480 It's not about adding another tool. 74 00:02:48,480 --> 00:02:52,240 It's about removing the friction of managing a dozen separate services. 75 00:02:52,240 --> 00:02:54,360 The key organizational unit, Foundry Projects. 76 00:02:54,360 --> 00:02:55,960 So you've got your AI factory. 77 00:02:55,960 --> 00:02:57,440 But a factory needs organization. 78 00:02:57,440 --> 00:02:58,920 That's where Foundry Projects come in. 79 00:02:58,920 --> 00:03:02,920 A Foundry Project is an isolated workspace for a specific AI solution. 80 00:03:02,920 --> 00:03:06,200 If Foundry is the factory, projects are individual workbenches. 81 00:03:06,200 --> 00:03:08,800 Each has its own tools, materials, and workers. 82 00:03:08,800 --> 00:03:12,280 Without interfering with each other, the team building a customer support chatbot won't 83 00:03:12,280 --> 00:03:16,400 accidentally mess up the HR Assistant project, what lives inside a project. 84 00:03:16,400 --> 00:03:20,360 Its own model deployments, agents, knowledge stores, evaluations, and monitoring, everything 85 00:03:20,360 --> 00:03:23,080 you need for one solution, all in one place. 86 00:03:23,080 --> 00:03:24,520 Here's the core structure. 87 00:03:24,520 --> 00:03:26,280 Projects live inside a Foundry resource. 88 00:03:26,280 --> 00:03:28,520 The resource is the building and security boundary. 89 00:03:28,520 --> 00:03:29,680 The building. 90 00:03:29,680 --> 00:03:30,840 Projects are the rooms inside. 91 00:03:30,840 --> 00:03:33,920 One building, one front door, one security desk, one build. 92 00:03:33,920 --> 00:03:36,040 But each room has its own purpose and its own team. 93 00:03:36,040 --> 00:03:40,840 For example, a company called Contoso builds three AI solutions, a customer support agent, 94 00:03:40,840 --> 00:03:43,640 an HR Assistant, and a document processing system. 95 00:03:43,640 --> 00:03:46,320 With Foundry they create one resource for the whole company. 96 00:03:46,320 --> 00:03:49,960 When they create three projects inside it, each project has its own models, agents, and 97 00:03:49,960 --> 00:03:51,240 knowledge stores. 98 00:03:51,240 --> 00:03:53,760 Teams work independently without stepping on each other's toes. 99 00:03:53,760 --> 00:03:56,400 This matters because different teams need different access. 100 00:03:56,400 --> 00:03:59,400 The customer support team doesn't need to see HR documents. 101 00:03:59,400 --> 00:04:01,880 The document processing team doesn't need refund policies. 102 00:04:01,880 --> 00:04:03,120 Projects give you that isolation. 103 00:04:03,120 --> 00:04:08,560 At the same time, IT keeps central control, managing one resource, one security policy, 104 00:04:08,560 --> 00:04:11,680 one budget, team autonomy with central governance. 105 00:04:11,680 --> 00:04:14,320 The heart of Foundry, the model catalog. 106 00:04:14,320 --> 00:04:15,560 Think of it this way. 107 00:04:15,560 --> 00:04:17,200 Every project needs a brain. 108 00:04:17,200 --> 00:04:19,360 In Foundry, that brain lives inside the model catalog. 109 00:04:19,360 --> 00:04:21,680 Over 11,000 models, and that's not a typo. 110 00:04:21,680 --> 00:04:27,280 You get open AI models like GPT-40 and GPT-4.1, Meta's Lama family, Mistral Deepseek, 111 00:04:27,280 --> 00:04:32,800 Microsoft's own five models, and topics Claude, Cohier, models for text, images, code, and 112 00:04:32,800 --> 00:04:33,800 embeddings. 113 00:04:33,800 --> 00:04:38,120 Right now, this is the largest model catalog you'll find on any cloud platform. 114 00:04:38,120 --> 00:04:41,640 Let's break those into two basic types, large language models and small language models. 115 00:04:41,640 --> 00:04:46,720 Large ones have billions of parameters GPT-4 has over a trillion, so they can handle complex 116 00:04:46,720 --> 00:04:52,440 reasoning, creative writing, and nuanced conversation, but they're expensive and slow. 117 00:04:52,440 --> 00:04:54,600 Small models have far fewer parameters. 118 00:04:54,600 --> 00:05:00,240 Microsoft's 5.3 has about 3.8 billion, tiny compared to GPT-4, but it's fast and cheap. 119 00:05:00,240 --> 00:05:04,920 For focused tasks like extracting data from a form or answering a simple question, it works 120 00:05:04,920 --> 00:05:05,920 great. 121 00:05:05,920 --> 00:05:06,920 Here's the practical benefit. 122 00:05:06,920 --> 00:05:08,400 You can deploy a model in seconds. 123 00:05:08,400 --> 00:05:11,280 Pick one from the catalog, click deploy, and it's ready. 124 00:05:11,280 --> 00:05:15,800 No provisioning hardware, no configuring networking, no waiting hours for setup, seconds, and before 125 00:05:15,800 --> 00:05:18,240 you commit, you can test it in the playground. 126 00:05:18,240 --> 00:05:23,280 Try different prompts, adjust settings like temperature and max tokens, see responses instantly. 127 00:05:23,280 --> 00:05:24,960 It's a test drive before you buy the car. 128 00:05:24,960 --> 00:05:29,960 You're not locked into one model, and that's a huge advantage over using OpenAI's API directly. 129 00:05:29,960 --> 00:05:35,040 With Foundry, you can use GPT-4O for complex reasoning where quality matters, FI for simple 130 00:05:35,040 --> 00:05:39,720 classification where speed and cost matter, and Meta's Lama for compliance if you need 131 00:05:39,720 --> 00:05:41,920 to run models in your own data center. 132 00:05:41,920 --> 00:05:43,600 You switch depending on the job. 133 00:05:43,600 --> 00:05:48,200 Foundry also includes a comparison tool that helps you evaluate models side by side. 134 00:05:48,200 --> 00:05:52,060 Compare by quality how accurate other responses by safety, how well does it resist prompt 135 00:05:52,060 --> 00:05:56,960 injection attacks, by cost, the price per million tokens, by speed, how fast does it generate 136 00:05:56,960 --> 00:05:57,960 output. 137 00:05:57,960 --> 00:06:01,240 That turns model selection from a guessing game into an informed decision. 138 00:06:01,240 --> 00:06:05,680 You pick the right tool for the right job, not just the one everyone's talking about. 139 00:06:05,680 --> 00:06:08,160 Building AI agents foundry's killer feature. 140 00:06:08,160 --> 00:06:10,120 A model alone isn't very useful. 141 00:06:10,120 --> 00:06:14,080 You can chat with it in the playground, ask questions, see what it generates, but that's 142 00:06:14,080 --> 00:06:15,080 just a conversation. 143 00:06:15,080 --> 00:06:18,920 The real magic happens when you build something that can actually do things, and that's where 144 00:06:18,920 --> 00:06:19,920 agents come in. 145 00:06:19,920 --> 00:06:21,640 And agent is more than a chatbot. 146 00:06:21,640 --> 00:06:23,120 A chatbot answers questions. 147 00:06:23,120 --> 00:06:24,560 An agent takes action. 148 00:06:24,560 --> 00:06:29,120 It can search the web, read files, run calculations, remember what you told it last week, 149 00:06:29,120 --> 00:06:30,800 and follow a set of instructions. 150 00:06:30,800 --> 00:06:32,680 It's like having an assistant who doesn't just talk. 151 00:06:32,680 --> 00:06:34,800 They actually get stuff done. 152 00:06:34,800 --> 00:06:36,920 Foundry gives you five components to build an agent. 153 00:06:36,920 --> 00:06:37,920 Let me walk through each one. 154 00:06:37,920 --> 00:06:39,400 First, instructions. 155 00:06:39,400 --> 00:06:42,720 This is where you tell the agent what it should do and how it should behave. 156 00:06:42,720 --> 00:06:44,040 Think of it like a job description. 157 00:06:44,040 --> 00:06:46,440 You might say, you are a customer support agent. 158 00:06:46,440 --> 00:06:47,840 Be friendly and professional. 159 00:06:47,840 --> 00:06:50,000 Only answer questions about our return policy. 160 00:06:50,000 --> 00:06:53,720 If you don't know the answer, say so, and escalate to a human. 161 00:06:53,720 --> 00:06:56,400 Without instructions, the agent does whatever the model feels like. 162 00:06:56,400 --> 00:06:58,400 With instructions, it has a clear purpose. 163 00:06:58,400 --> 00:06:59,480 Second, the model. 164 00:06:59,480 --> 00:07:00,480 This is the brain. 165 00:07:00,480 --> 00:07:03,240 It processes every request and generates responses. 166 00:07:03,240 --> 00:07:08,120 You pick which model to use, GPT-40 for complex reasoning, five for simple tasks, whatever 167 00:07:08,120 --> 00:07:09,120 fits. 168 00:07:09,120 --> 00:07:11,360 The model makes the agent smart, but it's only one piece. 169 00:07:11,360 --> 00:07:12,360 Third, tools. 170 00:07:12,360 --> 00:07:14,160 This is where the agent gets its hands. 171 00:07:14,160 --> 00:07:16,440 Tools are capabilities it can use to do things. 172 00:07:16,440 --> 00:07:18,480 Web search lets it look up current information. 173 00:07:18,480 --> 00:07:21,440 Code interpreter lets it do math and run calculations. 174 00:07:21,440 --> 00:07:23,680 File search lets it read documents you upload. 175 00:07:23,680 --> 00:07:25,880 Without tools, the agent can only generate text. 176 00:07:25,880 --> 00:07:27,960 With tools, it can act on the world. 177 00:07:27,960 --> 00:07:28,960 Fourth, knowledge. 178 00:07:28,960 --> 00:07:30,960 This is the information the agent can reference. 179 00:07:30,960 --> 00:07:35,480 You upload documents, PDFs, JSON files, whatever data your agent needs to answer questions 180 00:07:35,480 --> 00:07:36,480 accurately. 181 00:07:36,480 --> 00:07:40,880 Foundry uses Azure AI search behind the scenes to index that data and make it searchable. 182 00:07:40,880 --> 00:07:44,280 When a customer asks about a specific policy, the agent doesn't guess, it finds the actual 183 00:07:44,280 --> 00:07:45,480 document and reads it. 184 00:07:45,480 --> 00:07:46,760 Fifth, memory. 185 00:07:46,760 --> 00:07:49,080 This lets the agent remember across conversations. 186 00:07:49,080 --> 00:07:51,640 You talk to it today and it remembers your preferences. 187 00:07:51,640 --> 00:07:53,960 You come back tomorrow and it picks up where you left off. 188 00:07:53,960 --> 00:07:58,680 It stores chat summaries and user preferences, so the experience feels personal and continuous. 189 00:07:58,680 --> 00:08:00,920 Here's a concrete example from the research I did. 190 00:08:00,920 --> 00:08:02,960 I wrote a gift tracker agent in Foundry. 191 00:08:02,960 --> 00:08:04,200 The idea is simple. 192 00:08:04,200 --> 00:08:07,640 You want to keep track of gifts for your family, what you've given them in the past, what they 193 00:08:07,640 --> 00:08:10,240 like, what they don't like and how much you're spending. 194 00:08:10,240 --> 00:08:13,440 So the agent has instructions that say, you help plan gifts. 195 00:08:13,440 --> 00:08:15,800 Never suggest something they already received. 196 00:08:15,800 --> 00:08:20,560 It uses GPT-40 as its brain, web search to find gift ideas online, file search loaded 197 00:08:20,560 --> 00:08:22,520 with profiles for each family member. 198 00:08:22,520 --> 00:08:25,680 They're likes, dislikes and gift history from the last three years. 199 00:08:25,680 --> 00:08:29,200 Code interpreter to calculate budgets and totals and memory. 200 00:08:29,200 --> 00:08:33,440 So next time you open a conversation, it remembers you were looking for a birthday gift for your 201 00:08:33,440 --> 00:08:34,440 son Adam. 202 00:08:34,440 --> 00:08:36,960 You build and test all of this in the agent playground. 203 00:08:36,960 --> 00:08:40,720 It's a visual interface where you configure each component, test the agent's responses and 204 00:08:40,720 --> 00:08:42,840 see exactly which tools it used and why. 205 00:08:42,840 --> 00:08:44,760 Once it's working, you can deploy it. 206 00:08:44,760 --> 00:08:48,520 Foundry gives you an API endpoint you can call from any application or you can publish 207 00:08:48,520 --> 00:08:51,520 it directly to Microsoft Teams as a co-pilot agent. 208 00:08:51,520 --> 00:08:56,200 Either way, your agent goes from idea to production without writing a single line of infrastructure 209 00:08:56,200 --> 00:08:57,200 code. 210 00:08:57,200 --> 00:08:59,480 Under the hood, the architecture made simple. 211 00:08:59,480 --> 00:09:00,720 That's the surface level. 212 00:09:00,720 --> 00:09:02,640 But what's actually going on under the hood? 213 00:09:02,640 --> 00:09:04,360 Here's the architecture in plain English. 214 00:09:04,360 --> 00:09:08,360 When you create a Foundry resource, Azure sets up the whole infrastructure automatically 215 00:09:08,360 --> 00:09:12,560 so you don't need to provision storage, configure logging or setup security groups. 216 00:09:12,560 --> 00:09:16,440 Everything happens behind the scenes, creating a storage account for your files and logs, setting 217 00:09:16,440 --> 00:09:18,560 up monitoring and configuring identity. 218 00:09:18,560 --> 00:09:21,040 So all you do is create the resource and start building. 219 00:09:21,040 --> 00:09:25,680 Now Foundry uses the cognitive services resource type, the same one used by Azure Open AI 220 00:09:25,680 --> 00:09:28,360 and the older AI services like vision and speech. 221 00:09:28,360 --> 00:09:32,720 The old hub-based architecture used machine learning services, which is a completely different 222 00:09:32,720 --> 00:09:35,800 provider with different billing, networking and governance. 223 00:09:35,800 --> 00:09:40,320 That matters because Foundry lives in a simpler, more unified part of Azure's infrastructure. 224 00:09:40,320 --> 00:09:43,680 Projects are child resources of the Foundry resource, so they share the same identity and 225 00:09:43,680 --> 00:09:44,760 security boundary. 226 00:09:44,760 --> 00:09:48,400 When you set up access control at the Foundry level, it flows down to all projects, meaning 227 00:09:48,400 --> 00:09:51,440 you don't need to configure security separately for each one. 228 00:09:51,440 --> 00:09:55,160 But if you need project specific permissions, you can add those too. 229 00:09:55,160 --> 00:09:59,360 Azure doesn't work alone, it connects to other Azure services and Azure AI searches the 230 00:09:59,360 --> 00:10:03,520 most important one because that's how your agents find and retrieve knowledge. 231 00:10:03,520 --> 00:10:08,440 Key Vault stores, secrets and API keys securely and storage accounts hold files, logs and 232 00:10:08,440 --> 00:10:09,720 vector indexes. 233 00:10:09,720 --> 00:10:13,080 These connections are managed through what Foundry calls connections, which are pre-configured 234 00:10:13,080 --> 00:10:18,680 links that let your agents access these resources without you having to manage credentials manually. 235 00:10:18,680 --> 00:10:22,120 The Foundry agent service is the runtime that actually hosts your agents. 236 00:10:22,120 --> 00:10:26,000 You can check it as the engine room. 237 00:10:26,000 --> 00:10:30,720 When a user sends a request, the agent service handles the coordination, receiving the 238 00:10:30,720 --> 00:10:36,120 request, checking the instructions, calling the model, invoking the right tools, retrieving 239 00:10:36,120 --> 00:10:38,120 knowledge and generating a response. 240 00:10:38,120 --> 00:10:43,120 It also runs safety filters to catch harmful content and handles scaling, so if a thousand 241 00:10:43,120 --> 00:10:45,760 users hit your agent at once, it spins up capacity automatically. 242 00:10:45,760 --> 00:10:50,400 Everything runs inside Azure's enterprise grade security, where EntraID handles authentication, 243 00:10:50,400 --> 00:10:53,800 which overall ensures only authorized people can modify agents. 244 00:10:53,800 --> 00:10:56,320 Content safety filters block inappropriate responses. 245 00:10:56,320 --> 00:11:00,320 It's the same security infrastructure that runs Microsoft's own services. 246 00:11:00,320 --> 00:11:03,600 One more thing, the old Hub and Project architecture still exists. 247 00:11:03,600 --> 00:11:07,120 If you've been using Azure AI Foundry for a while, you might have Hub set up and those 248 00:11:07,120 --> 00:11:11,040 still work with Microsoft support, but they won't get new agent features like the agent 249 00:11:11,040 --> 00:11:14,560 playground, the agent service or the memory capabilities. 250 00:11:14,560 --> 00:11:17,920 All of that only comes to the new Foundry projects, so if you're starting fresh or want 251 00:11:17,920 --> 00:11:20,120 to build agents, use the new projects. 252 00:11:20,120 --> 00:11:24,400 You can't build on the old foundation, how everything connects, the big picture. 253 00:11:24,400 --> 00:11:26,960 So what actually happens when someone uses a Foundry application? 254 00:11:26,960 --> 00:11:30,200 Imagine a user types a question and that question hits your agent. 255 00:11:30,200 --> 00:11:34,480 The agent checks its instructions, picks the right model and starts processing, but it 256 00:11:34,480 --> 00:11:36,160 doesn't just guess the answer. 257 00:11:36,160 --> 00:11:40,440 It reaches into Azure AI search to find relevant knowledge from your documents, calls a tool 258 00:11:40,440 --> 00:11:45,280 like web search for current information, processes all that context and generates a response. 259 00:11:45,280 --> 00:11:48,520 Then it stores the conversation in memory, so next time it remembers who you are and what 260 00:11:48,520 --> 00:11:49,520 you discussed. 261 00:11:49,520 --> 00:11:51,760 And that's the end to end flow. 262 00:11:51,760 --> 00:11:56,320 Before Foundry, you needed five separate Azure services and custom code to make that work. 263 00:11:56,320 --> 00:12:00,680 Azure Open AI for the model, AI search for knowledge retrieval, a separate service for 264 00:12:00,680 --> 00:12:04,520 web search, storage for logs and files, and key vault for secrets. 265 00:12:04,520 --> 00:12:09,240 Then you had to write code to wire all of that together, handle authentication, manage errors, 266 00:12:09,240 --> 00:12:10,760 and set up monitoring. 267 00:12:10,760 --> 00:12:12,200 Foundry does all of that out of the box. 268 00:12:12,200 --> 00:12:14,720 You just configure the components and your agent is running. 269 00:12:14,720 --> 00:12:16,400 Here's the thing, everything interacts. 270 00:12:16,400 --> 00:12:19,680 Change the model and you change the cost and accuracy of every response. 271 00:12:19,680 --> 00:12:22,760 Add better knowledge sources and your answers get more accurate. 272 00:12:22,760 --> 00:12:26,320 Enable memory and the experience feels personal instead of robotic. 273 00:12:26,320 --> 00:12:28,000 These aren't isolated decisions. 274 00:12:28,000 --> 00:12:29,000 They're a system. 275 00:12:29,000 --> 00:12:32,600 And Foundry gives you the controls to tune that system for your specific needs. 276 00:12:32,600 --> 00:12:35,480 This is why enterprises are moving from pilots to production. 277 00:12:35,480 --> 00:12:39,160 Foundry gives them the scaffolding to build trustworthy AI applications. 278 00:12:39,160 --> 00:12:41,880 Security is built in, monitoring is built in, and evaluation is built in. 279 00:12:41,880 --> 00:12:43,120 You're not starting from scratch. 280 00:12:43,120 --> 00:12:47,680 You're standing on a platform that handles the hard parts so you can focus on the application. 281 00:12:47,680 --> 00:12:49,680 Getting started, your first actions. 282 00:12:49,680 --> 00:12:50,880 So where do you start? 283 00:12:50,880 --> 00:12:55,000 Head over to AI, Azure.com and create your first Foundry resource. 284 00:12:55,000 --> 00:12:58,600 You'll need an Azure subscription, but if you don't have one, sign up for a free account 285 00:12:58,600 --> 00:13:00,320 with some credits to play around with. 286 00:13:00,320 --> 00:13:02,280 The whole setup takes about a minute. 287 00:13:02,280 --> 00:13:05,880 Next, create a project inside that resource and give it a name that matches whatever you're 288 00:13:05,880 --> 00:13:06,880 building. 289 00:13:06,880 --> 00:13:08,160 Then pick a model from the catalog. 290 00:13:08,160 --> 00:13:13,000 I'd recommend starting with GPT-4 Omini because it's cheap, fast, and capable enough for 291 00:13:13,000 --> 00:13:14,160 most experiments. 292 00:13:14,160 --> 00:13:15,920 You can deploy it with one click. 293 00:13:15,920 --> 00:13:19,240 After that, open the agent playground and build a simple agent. 294 00:13:19,240 --> 00:13:23,560 Give it instructions like, you are a helpful assistant that answers questions about company 295 00:13:23,560 --> 00:13:26,560 policies, then add web search as a tool. 296 00:13:26,560 --> 00:13:30,640 Test it by asking a question and watching the trace to see which tools it used and why. 297 00:13:30,640 --> 00:13:32,480 Don't try to build everything at once. 298 00:13:32,480 --> 00:13:36,080 Pick one use case, maybe a document Q&A bought that answers questions about your company 299 00:13:36,080 --> 00:13:37,840 handbook and starts more. 300 00:13:37,840 --> 00:13:39,480 Edit working, then expand from there. 301 00:13:39,480 --> 00:13:43,520 Add file search with actual documents, add memory, so it remembers users, and add code 302 00:13:43,520 --> 00:13:45,200 interpreter if you need calculations. 303 00:13:45,200 --> 00:13:46,200 Here's the thing. 304 00:13:46,200 --> 00:13:49,640 Foundry includes a built-in AI chatbot that answers questions about the platform itself. 305 00:13:49,640 --> 00:13:53,200 If you get stuck, ask it things like, what models are available in my region? 306 00:13:53,200 --> 00:13:55,480 Or, how do I add memory to my agent? 307 00:13:55,480 --> 00:13:59,080 It searches the documentation and gives you an answer instantly, like having a support 308 00:13:59,080 --> 00:14:01,120 engineer build right into the portal. 309 00:14:01,120 --> 00:14:05,080 So that's the system, a unified platform that takes models, projects, agents, knowledge 310 00:14:05,080 --> 00:14:08,720 and security and wraps them all into something that works together. 311 00:14:08,720 --> 00:14:12,040 When you see how everything integrates, the confusion falls away. 312 00:14:12,040 --> 00:14:16,000 Foundry isn't just another tool, it's the workshop where you build AI applications. 313 00:14:16,000 --> 00:14:17,000 Here's the bottom line. 314 00:14:17,000 --> 00:14:18,800 Your biggest win today is simple. 315 00:14:18,800 --> 00:14:21,040 Create a Foundry project and build one agent. 316 00:14:21,040 --> 00:14:22,040 That's it. 317 00:14:22,040 --> 00:14:25,040 If you found this helpful, subscribe for more Plane English breakdowns like this and 318 00:14:25,040 --> 00:14:28,720 share this episode with someone who's also confused about Microsoft's AI lineup.