Introducing the Microsoft Agent Framework
Introducing the Microsoft Agent Framework: A Unified Foundation for AI Agents and Workflows The landscape of AI development is evolving rapidly, and Microsoft is at the forefront with the release of the Microsoft Agent Framework an open-source SDK designed to empower developers to build intelligent, multi-agent systems with ease and precision. Whether you're working in .NET or Python, this framework offers a unified, extensible foundation that merges the best of Semantic Kernel and AutoGen, while introducing powerful new capabilities for agent orchestration and workflow design. Introducing Microsoft Agent Framework: The Open-Source Engine for Agentic AI Apps | Azure AI Foundry Blog Introducing Microsoft Agent Framework | Microsoft Azure Blog Why Another Agent Framework? Both Semantic Kernel and AutoGen have pioneered agentic development, Semantic Kernel with its enterprise-grade features and AutoGen with its research-driven abstractions. The Microsoft Agent Framework is the next generation of both, built by the same teams to unify their strengths: AutoGen’s simplicity in multi-agent orchestration. Semantic Kernel’s robustness in thread-based state management, telemetry, and type safety. New capabilities like graph-based workflows, checkpointing, and human-in-the-loop support This convergence means developers no longer have to choose between experimentation and production. The Agent Framework is designed to scale from single-agent prototypes to complex, enterprise-ready systems Core Capabilities AI Agents AI agents are autonomous entities powered by LLMs that can process user inputs, make decisions, call tools and MCP servers, and generate responses. They support providers like Azure OpenAI, OpenAI, and Azure AI, and can be enhanced with: Agent threads for state management. Context providers for memory. Middleware for action interception. MCP clients for tool integration Use cases include customer support, education, code generation, research assistance, and more—especially where tasks are dynamic and underspecified. Workflows Workflows are graph-based orchestrations that connect multiple agents and functions to perform complex, multi-step tasks. They support: Type-based routing Conditional logic Checkpointing Human-in-the-loop interactions Multi-agent orchestration patterns (sequential, concurrent, hand-off, Magentic) Workflows are ideal for structured, long-running processes that require reliability and modularity. Developer Experience The Agent Framework is designed to be intuitive and powerful: Installation: Python: pip install agent-framework .NET: dotnet add package Microsoft.Agents.AI Integration: Works with Foundry SDK, MCP SDK, A2A SDK, and M365 Copilot Agents Samples and Manifests: Explore declarative agent manifests and code samples Learning Resources: Microsoft Learn modules AI Agents for Beginners AI Show demos Azure AI Foundry Discord community Migration and Compatibility If you're currently using Semantic Kernel or AutoGen, migration guides are available to help you transition smoothly. The framework is designed to be backward-compatible where possible, and future updates will continue to support community contributions via the GitHub repository. Important Considerations The Agent Framework is in public preview. Feedback and issues are welcome on the GitHub repository. When integrating with third-party servers or agents, review data sharing practices and compliance boundaries carefully. The Microsoft Agent Framework marks a pivotal moment in AI development, bringing together research innovation and enterprise readiness into a single, open-source foundation. Whether you're building your first agent or orchestrating a fleet of them, this framework gives you the tools to do it safely, scalably, and intelligently. Ready to get started? Download the SDK, explore the documentation, and join the community shaping the future of AI agents.How do I choose the right model for my agent?
Welcome back to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a real question from the community with simple, practical guidance to help you build smarter agents. Today’s question comes from a developer that’s right at the beginning of their agent building journey and needs a little help choosing a model. 💬 Dear Agent Support I’m overwhelmed by all the model options out there. Some are small, some are huge. Some are free, some cost a lot. Some say “multimodal” but I’m not sure if I need that. How do I choose the right model for my agent? Great question! Model choice is one of the most important design decisions you’ll make. Pick something too small, and your agent may struggle with complex tasks. Go too big, and you could be paying for power you don’t need. Let’s break down the key factors to consider. 🧩 Capabilities vs. Use Case The first—and most important—question isn’t which model is “best.” It’s what does my agent actually need to do? Here’s a few angles to think through: Input and Output Types Will your agent only handle text, or does it need to process other formats like images, audio, or structured data? Models differ in how many modalities they support and in how well they can handle outputs that must follow strict formatting. Complexity of Tasks Simple, transactional tasks (like pulling information from a document or answering straightforward queries) don’t require the same reasoning depth as tasks that involve planning, multi-step logic, or open-ended creativity. Define the level of reasoning and adaptability your agent needs. Control Requirements Some agents need highly controlled outputs (think JSON schemas for downstream services), while others benefit from free-form creativity. The degree of control you need (i.e. structured output, function calling, system prompt) should guide model choice. Domain Knowledge Does your agent operate in a general-purpose domain, or does it need strong understanding of a specific area (like legal, medical, or technical documentation)? Consider whether you’ll rely on the model’s built-in knowledge, retrieval from external sources, or fine-tuning for domain expertise. Interaction Style Will users interact with the agent in short, direct prompts, or longer, conversational exchanges? Some models handle chat-like, multi-turn contexts better than others, while others excel at single-shot completions. In short: start by mapping out your agent’s needs in terms of data types, reasoning depth, control, domain, and interaction style. Once you have that picture, it’s much easier to narrow down which models are a genuine fit, and which ones would be mismatched. ⚖️ Performance vs. Cost Once you know what your agent needs to do, the next trade-off is between performance and cost. Bigger models are often more capable, but they also come with higher latency, usage costs, and infrastructure requirements. The trick is to match “enough performance” to the real-world expectations for your agent. Here are some factors to weigh: Task Complexity vs. Model Size If your agent’s tasks involve nuanced reasoning, long-context conversations, or open-ended problem solving, a more capable (and often larger) model may be necessary. On the other hand, for lightweight lookups or structured Q&A, a smaller model can perform just as well, and more efficiently. Response Time Expectations Latency matters. A model that takes 8–10 seconds to respond may be fine in a batch-processing workflow but frustrating in a real-time chat interface. Think about how quickly your users expect the agent to respond and whether you’re willing to trade speed for accuracy. Budget and Token Costs Larger models consume more tokens per request, which translates to higher costs, especially if your agent will scale to many users. Consider both per-request cost and aggregate monthly cost based on expected usage volume. Scaling Strategy Some developers use a “tiered” approach: route simple queries to a smaller, cheaper model and reserve larger models for complex tasks. This can balance performance with budget without compromising user experience. The Azure AI Founder Model Router performs in a similar manner. Experimentation Over Assumptions Don’t assume the largest model is always required. Start with something mid-range, test it against your use case, and only scale up if you see gaps. This iterative approach often prevents overspending. At the end of the day, performance isn’t about squeezing the most power out of a model, it’s about choosing the right amount of capability for the job, without paying for what you don’t need. 🔑 Licensing and Access Even if you’ve found a model that looks perfect on paper, practical constraints around access and licensing can make or break your choice. These considerations often get overlooked until late in the process, but they can have big downstream impacts. A few things to keep in mind: Where the Model Lives Some models are only accessible through a hosted API (like on a cloud provider), while others are open source and can be self-hosted. Hosted APIs are convenient and handle scaling for you, but they also lock you into availability, pricing, and rate limits set by the provider. Self-hosting gives you control, but also means managing infrastructure, updates, and security yourself. Terms of Use Pay attention to licensing restrictions. Some providers limit usage for commercial products, sensitive data, or high-risk domains (like healthcare or finance). Others may require explicit consent or premium tiers to unlock certain capabilities. Data Handling and Privacy If your agent processes sensitive or user-specific data, you’ll need to confirm whether the model provider logs, stores, or uses data for training. Check for features like “no data retention” modes, private deployments, or enterprise SLAs if compliance is critical. Regional Availability Certain models or features may only be available in specific regions due to infrastructure or regulatory constraints. This matters if your users are global, or if you need to comply with data residency laws (e.g., keeping data in the EU). Support for Deployment Options Consider whether the model can be deployed in the way you need—API-based integration, on-prem deployment, or edge devices. If you’re building something that runs locally (say, on a mobile app), an enormous cloud-only model won’t be practical. Longevity and Ecosystem Models evolve quickly. Some experimental models may not be supported long-term, while others are backed by a stable provider with ongoing updates. Think about how much you want to bet on a model that might disappear in six months versus one with a roadmap you can count on. Model choice isn’t just about capability and performance, it’s also about whether you can use it under the terms, conditions, and environments that your project requires. 🔍 Exploring Models with Azure AI Foundry Once you’ve thought through capabilities, performance trade-offs, and licensing, the next step is exploring what’s available to you. If you’re building with Azure, this is where the Azure AI Foundry Models becomes invaluable. Instead of guessing which model might fit, you can browse, filter, and compare options directly, complete with detailed model cards that outline features, intended use cases, and limitations. Think of the model catalog as your “shopping guide” for models: it helps you quickly spot which ones align with your agent’s needs and gives you the fine print before you commit. 🔁 Recap Here’s a quick rundown of what we covered: Start with capabilities. Match the model’s strengths to the inputs, outputs, and complexity your agent requires. Balance performance with cost. Bigger isn’t always better. Pick the right level of capability without overspending. Review licensing and access. Make sure the model is available in your region, permitted for your use case, and deployed in the way you need. Explore before you build. Use the Azure AI Foundry Model Catalog to filter options, read model cards, and test in the Playground. 📺 Want to Go Deeper? With so many new models available on an almost daily basis, it can be a challenge to keep up with what’s new! However, our Model Mondays series has you covered! Each week, we bring to you the latest news in AI models. We also recently launched our brand-new series: Inside Azure AI Foundry. In this series, we dive deep into the latest AI models, tools, and platform features — with practical demos and technical walkthroughs that show you how to integrate them into your workflows. It’s perfect for developers who want to see capabilities in action before deploying them in real projects. As always remember, your agent doesn’t need the “best” model on paper—it needs the right model for the job it’s designed to do.How do I catch bad data before it derails my agent?
How do I catch bad data before it derails my agent? When an agent relies on data that’s incomplete, inconsistent, or plain wrong, every downstream step inherits that problem. You will waste time debugging hallucinations that are actually caused by a stray “NULL” string, or re-running fine-tunes because of invisible whitespace in a numeric column. Even small quality issues can: Skew model evaluation metrics. Trigger exceptions in your application code. Undermine user trust when answers look obviously off. The bottom line is that a five-minute inspection up front can save hours later.How do I A/B test different versions of my agent?
We tend to think of A/B testing as a marketer’s tool (i.e. headline A vs. headline B). But it’s just as useful for developers building agents. Why? Because most agent improvements are experimental. You’re changing one or more of the following: the system prompt the model the tool selection the output format the interaction flow But without a structured way to test those changes, you’re just guessing. You might think your updated version is smarter or more helpful…but until you compare, you won’t know! A/B testing helps you turn instincts into insight and gives you real data to back your next decision.How do I give my agent access to tools?
Welcome back to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a real question from the community with simple, practical guidance to help you build smarter agents. Today’s question comes from someone trying to move beyond chat-only agents into more capable, action-driven ones: 💬 Dear Agent Support I want my agent to do more than just respond with text. Ideally, it could look up information, call APIs, or even run code—but I’m not sure where to start. How do I give my agent access to tools? This is exactly where agents start to get interesting! Giving your agent tools is one of the most powerful ways to expand what it can do. But before we get into the “how,” let’s talk about what tools actually mean in this context—and how Model Context Protocol (MCP) helps you use them in a standardized, agent-friendly way. 🛠️ What Do We Mean by “Tools”? In agent terms, a tool is any external function or capability your agent can use to complete a task. That might be: A web search function A weather lookup API A calculator A database query A custom Python script When you give an agent tools, you’re giving it a way to take action—not just generate text. Think of tools as buttons the agent can press to interact with the outside world. ⚡ Why Give Your Agent Tools? Without tools, an agent is limited to what it “knows” from its training data and prompt. It can guess, summarize, and predict, but it can’t do. Tools change that! With the right tools, your agent can: Pull live data from external systems Perform dynamic calculations Trigger workflows in real time Make decisions based on changing conditions It’s the difference between an assistant that can answer trivia questions vs. one that can book your travel or manage your calendar. 🧩 So… How Does This Work? Enter Model Context Protocol (MCP). MCP is a simple, open protocol that helps agents use tools in a consistent way—whether those tools live in your app, your cloud project, or on a server you built yourself. Here’s what MCP does: Describes tools in a standardized format that models can understand Wraps the function call + input + output into a predictable schema Lets agents request tools as needed (with reasoning behind their choices) This makes it much easier to plug tools into your agent workflow without reinventing the wheel every time! 🔌 How to Connect an Agent to Tools Wiring tools into your agent might sound complex, but it doesn’t have to be! If you’ve already got a MCP server in mind, there’s a straightforward way within the AI Toolkit to expose it as a tool your agent can use. Here’s how to do it: Open the Agent Builder from the AI Toolkit panel in Visual Studio Code. Click the + New Agent button and provide a name for your agent. Select a Model for your agent. Within the Tools section, click + MCP Server. In the wizard that appears, click + Add Server. From there, you can select one of the MCP servers built my Microsoft, connect to an existing server that’s running, or even create your own using a template! After giving the server a Server ID, you’ll be given the option to select which tools from the server to add for your agent. Once connected, your agent can call tools dynamically based on the task at hand. 🧪 Test Before You Build Once you’ve connected your agent to an MCP server and added tools, don’t jump straight into full integration. It’s worth taking time to test whether the agent is calling the right tool for the job. You can do this directly in the Agent Builder: enter a test prompt that should trigger a tool in the User Prompt field, click Run, and observe how the model responds. This gives you a quick read on tool call accuracy. If the agent selects the wrong tool, it’s a sign that your system prompt might need tweaking before you move forward. However, if the agent calls the correct tool but the output still doesn’t look right, take a step back and check both sides of the interaction. It might be that the system prompt isn’t clearly guiding the agent on how to use or interpret the tool’s response. But it could also be an issue with the tool itself—whether that’s a bug in the logic, unexpected behavior, or a mismatch between input and expected output. Testing both the tool and the prompt in isolation can help you pinpoint where things are going wrong before you move on to full integration. 🔁 Recap Here’s a quick rundown of what we covered: Tools = external functions your agent can use to take action MCP = a protocol that helps your agent discover and use those tools reliably If the agent calls the wrong tool—or uses the right tool incorrectly—check your system prompt and test the tool logic separately to isolate the issue. 📺 Want to Go Deeper? Check out my latest video on how to connect your agent to a MCP server—it’s part of the Build an Agent Series, where I walk through the building blocks of turning an idea into a working AI agent. The MCP for Beginners curriculum covers all the essentials—MCP architecture, creating and debugging servers, and best practices for developing, testing, and deploying MCP servers and features in production environments. It also includes several hands-on exercises across .NET, Java, TypeScript, JavaScript and Python. 👉 Explore the full curriculum: aka.ms/AITKmcp And for all your general AI and AI agent questions, join us in the Azure AI Foundry Discord! You can find me hanging out there answering your questions about the AI Toolkit. I'm looking forward to chatting with you there! Whether you’re building a productivity agent, a data assistant, or a game bot—tools are how you turn your agent from smart to useful.How do I get my agent to respond in a structured format like JSON?
Welcome back to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a real question from the community with simple, practical guidance to help you build smarter agents. 💬 Dear Agent Support I’m building an agent that feeds its response into another app—but sometimes the output is messy or unpredictable. Can you help? This looks like a job for JSON! While agent output often comes back as plain text, there are times when you need something more structured, especially if another system, app, or service needs to reliably parse and use that response. 🧠 What’s the Deal with JSON Output? If your agent is handing off data to another app, you need the output to be clean, consistent, and easy to parse. That’s where JSON comes in. JSON (JavaScript Object Notation) is a lightweight, widely supported format that plays nicely with almost every modern tool or platform. Whether your agent is powering a dashboard, triggering a workflow, or sending data into a UI component, JSON makes the handoff smooth. You can define exactly what shape the response should take (i.e. keys, values, types) so that other parts of your system know what to expect every single time. Without it? Things get messy fast! Unstructured text can vary wildly from one response to the next. A missing field, a misaligned format, or even just an unexpected line break can break downstream logic, crash a frontend, or silently cause bugs you won’t catch until much later. Worse, you end up writing brittle post-processing code to clean up the output just to make it usable. The bottom line: If you want your agent to work well with others, it needs to speak in a structured format. JSON isn’t just a nice-to-have, it’s the language of interoperability. 🧩 When You’d Want Structured Output Not every agent needs to speak JSON, but if your response is going anywhere beyond the chat window, structured output is your best bet. Let’s say your agent powers a dashboard. You might want to display the response in different UI components—a title, a summary, maybe a set of bullet points. That only works if the response is broken into predictable parts. Same goes for workflows. If you’re passing the output into another service, like Power Automate, an agent framework like Semantic Kernal, or even another agent, it needs to follow a format those systems can recognize. Structured output also makes logging and debugging easier. When every response follows the same format, you can spot problems faster. Missing fields, weird values, or unexpected data types stand out immediately. It also future proofs your agent. If you want to add new features later, like saving responses to a database or triggering different actions based on the content, having structured output gives you the flexibility to build on top of what’s already there. If the output is meant to do more than just be read by a human, it should be structured for a machine. 📄 How to Define a JSON Format Once you know your agent’s output needs to be structured, the next step is telling the model exactly how to structure it. That’s where defining a schema comes in. In this context, a schema is just a blueprint for the shape of your data. It outlines what fields you expect, what type of data each one should hold, and how everything should be organized. Think of it like a form template: the model just needs to fill in the blanks. Here’s a simple example of a JSON schema for a to-do app: { "task": "string", "priority": "high | medium | low", "due_date": "YYYY-MM-DD" } Once you have your format in mind, include it directly in your prompt. But if you’re using the AI Toolkit in Visual Studio Code, you don’t have to do this manually every time! 🔧 Create a JSON schema with the AI Toolkit The Agent Builder feature supports structured output natively. You can provide a JSON schema alongside your prompt, and the agent will automatically aim to match that format. This takes the guesswork out of prompting. Instead of relying on natural language instructions to shape the output, you’re giving the model a concrete set of instructions to follow. Here’s how to do it: Open the Agent Builder from the AI Toolkit panel in Visual Studio Code. Click the + New Agent button and provide a name for your agent. Select a Model for your agent. Within the System Prompt section, enter: You recommend a movie to watch. Within the User Prompt section, enter: Recommend a science-fiction movie with robots. In the Structured Output section, select json_schema as the output format. Click Prepare schema. In the wizard, select Use an example. For the example, select paper_metadata. Save the file to your desired location. You can name the file: movie_metadata. In the Agent Builder, select movie_metadata to open the file. Using the template provided, modify the schema to format the title, genre, year, and reason. Once done, save the file. { "name": "movie_metadata", "strict": true, "schema": { "type": "object", "properties": { "title": { "type": "string" }, "genre": { "type": "array", "items": { "type": "string" } }, "year": { "type": "string" }, "reason": { "type": "array", "items": { "type": "string" } } }, "required": [ "title", "genre", "year", "reason" ], "additionalProperties": false } } And just like that, you’ve set up a JSON schema for your agent’s output! 🧪 Test Before You Build You can submit a prompt with the Agent Builder to validate whether the agent adheres to the JSON schema when returning its response. When you click Run, the agent’s response will appear in the Prompt tab, ideally in JSON format. 🔁 Recap Here’s a quick rundown of what we covered: JSON lets you create reliable, machine-friendly agent responses. JSON is essential for interoperability between apps, tools, and workflows. Without JSON, you risk fragile pipelines, broken features, or confusing bugs. The AI Toolkit supports including a JSON schema with your agent’s prompt. 📺 Want to Go Deeper? Check out the AI Agents for Beginners curriculum in which we dive a bit more into agentic design patterns which includes defining structured outputs. We’ll have a video landing soon in the Build an Agent Series that takes you through a step-by-step look of creating a JSON schema for your agent!What models can I use for free while prototyping?
Welcome back to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a real question from the community with simple, practical guidance to help you build smarter agents. Today’s question comes from someone still in the prototyping phase—looking to use free models until they’re ready to commit: 💬 Dear Agent Support I’m experimenting with different agent ideas, but I’m not ready to pay for API credits just yet. Are there any models I can use for free? Short answer: yes, and you’ve got a couple of good options! Let’s break them down. 🧠 GitHub Models: A Free Way to Experiment If you’re just getting started with agents and want a no-cost way to try things out, GitHub-hosted models are a great option. These models are maintained by the GitHub team and run entirely on GitHub’s infrastructure, so you don’t need to bring your own API key or worry about usage fees. They’re designed for prototyping and lightweight experimentation, making them ideal for testing out ideas, building proof-of-concepts, or just getting familiar with how agents and models interact. You can try them directly in the GitHub web interface or through tools like the AI Toolkit, which includes them in its Model Catalog. Many supports common features like structured output, chat history, and tool use, and are regularly updated to reflect community needs. Think of these as your training wheels: stable, reliable, and free to use while you explore what your agent can do. ⚠️ But Beware of Rate Limits Free models are great for prototyping…but there’s a catch! GitHub-hosted models come with usage limits, which means you might hit a wall if you’re testing frequently, building complex agents, or collaborating with teammates. These rate limits exist to ensure fair access for everyone using the shared infrastructure, especially during peak demand. If you’ve ever wondered why your responses stop, it’s probably because you’ve reached the cap for the day. The good news? GitHub recently introduced a Pay-As-You-Go option. This lets you continue using the same hosted models with more generous limits, only paying for what you use. It’s a helpful bridge for developers who’ve outgrown the free tier but aren’t ready to commit to a full API plan with another provider. If your agent is starting to feel constrained, this might be the right moment to switch gears. 🖥️ Want More Control? Run a Local Model If you’d rather skip rate limits altogether, or just prefer running things on your own machine, you could always use a local model. Local models give you full control over how the model runs, how often you use it, and what kind of hardware it runs on. There’s no API key, no usage tracking, and no hidden costs. It’s just you and the model, running side by side. You can download and host open-source models like LLaMA, Mistral, or Code Phi models using tools like Ollama or Foundry Local, which makes setup surprisingly simple. Most local models are optimized to run efficiently on consumer-grade hardware, so even a decent laptop can handle basic inference. This is especially handy if you're experimenting with sensitive data, need offline access, or want to test agents in environments where cloud isn’t an option. Of course, going local means you’re responsible for setup, performance tuning, and hardware compatibility, but for many developers, that tradeoff is worth it! 🧰 Ready to Try One Out? Whether you’re curious about GitHub-hosted models or want to use a local one, the Models feature within the AI Toolkit makes it easy to test them out, no custom setup required. With just a few clicks, you can browse available models, run test prompts in the Playground, and even use them with the agent you’re building. Here’s how to do it: Use a GitHub Model Open the Model Catalog from the AI Toolkit panel in Visual Studio Code. Click the Hosted by filter near the search bar. Select GitHub. Browse the filtered results. Select + Add Model to add a model to your list of models. Use a Local Model Note: Only Ollama and Custom ONNX models are currently supported. The instructions below only focus on adding local models from Ollama. Download your chosen local model to your computer. From the AI Toolkit panel in Visual Studio Code, hover over My Models and select the + icon. In the wizard, select Add Ollama Model. In the wizard, select Select models from Ollama library. This will provide a list of the models available in your local Ollama library (i.e. models you’ve downloaded). In the wizard, select the model(s) you want to connect and click OK. Your cost-free models are now available to use with your agent! If you’re unsure whether a model is a GitHub model or Ollama model, you can view its category within the My Models section of the AI Toolkit panel. The models within that section are organized by model source/host. 🧪 Test Before You Build Whether you’ve added a GitHub hosted model or a local model, you can chat with the models in the Playground or within the Agent Builder. The model is available for selection within the Model drop-down. As a reminder, GitHub hosted models have rate limits. If you hit a rate limit with a GitHub hosted model, the AI Toolkit will provide a notification presenting you with the option to either use GitHub pay-as-you-go models or Deploy to Azure AI Foundry for higher limits. Whichever path you choose, the AI Toolkit helps you prototype with confidence, giving you flexibility early on and clear upgrade paths when you're ready to scale! 🔁 Recap Here’s a quick rundown of what we covered: GitHub-hosted models let you start building fast, with no API keys or fees, but they do come with rate limits. GitHub Pay-As-You-Go gives you a way to scale up without switching tools. Local models give you full control and zero rate limits, just run them on your own machine using tools like Ollama. The AI Toolkit supports both options, letting you chat with models, test prompts, and build agents right inside VS Code. 📺 Want to Go Deeper? With so many models available these days, it can feel overwhelming to keep tabs on what’s available. Check out the Model Mondays series for all the latest news on language models! By the way, GitHub has guides on discovering and experimenting with free AI models; definitely worth a read if you want to understand what’s under the hood. Check out their articles on GitHub Models. No matter where you are in your agent journey, having free, flexible model options means you can spend more time building—and less time worrying about the bill.Learn Together: Building an MCP Server with AI Toolkit
AI is evolving fast—but building real, useful AI applications still requires more than just a smart model. That’s where the Model Context Protocol (MCP) comes in. MCP is an open standard that helps AI systems talk to tools, data, and services in a clean, reusable way. Think of it as the coordination layer that turns a smart model into an intelligent application. Whether you’re integrating APIs, calling scripts, or orchestrating multiple agents, MCP provides the structure that makes it all work together. And thanks to the AI Toolkit for Visual Studio Code, working with MCP is now easier than ever. The toolkit gives you an intuitive playground, built-in server tools, model integration, and everything you need to go from idea to working prototype without leaving your editor. 📅 Join the Event: Learn Together – Building an MCP Server with AI Toolkit In this live, hands-on workshop, we’ll walk through four practical modules designed to help you build, configure, and test your own MCP server from scratch. You’ll get to follow along step-by-step using the AI Toolkit extension for VS Code. Whether you're building multi-agent workflows, enabling tools in a RAG app, or just looking for a smarter way to connect your models to real-world actions, this workshop is for you. 🧪 What You’ll Learn – Inside the Labs Here’s a preview of the four modules we’ll cover during the livestream: Module 1: Getting Started with AI Toolkit In this lab, you’ll install and configure the AI Toolkit for Visual Studio Code, then dive into its key features—like the model catalog, interactive playground, and agent builder. You'll build your first functional agent, experiment with real-time model testing, and evaluate outputs using built-in metrics. Along the way, you’ll also explore batch processing and multimodal support. By the end, you’ll have a solid foundation in what the AI Toolkit can do. Module 2: Integrating MCP with Your Agents This lab introduces the Model Context Protocol (MCP) and shows you how to supercharge your AI agents with tool-using capabilities. You’ll connect to existing MCP servers, like the Playwright automation server, then integrate them into your agents using the AI Toolkit. From configuring tools to testing and deploying MCP-powered agents, you’ll see how external tools become part of your model’s workflow. This lab is your bridge between smart models and real-world actions. Module 3: Building and Debugging Custom MCP Servers Here, you’ll go beyond using prebuilt MCP servers and start creating your own. You’ll learn how to use the latest MCP Python SDK to configure and debug a custom server, including a weather tool example. The lab walks through setting up the MCP Inspector, testing tool calls, and running agents in a local dev environment. You’ll leave with a full understanding of how to build, test, and debug your own MCP endpoints. Module 4: Real-World MCP Server – GitHub Clone Agent In this final lab, you’ll build a GitHub clone MCP server that replicates real dev workflows. It features smart repo cloning, intelligent directory setup, error handling, and direct integration with VS Code. You’ll even enable GitHub Copilot’s Agent Mode to interact with your custom tools. This lab wraps everything together into a production-grade example of what’s possible with MCP. 💡 Why MCP + AI Toolkit? The magic of MCP is in its modularity. It gives developers a structured way to define tools, link models, and control interactions, all in a language-agnostic, reusable way. And when paired with the AI Toolkit, you get a guided development experience with built-in tools for testing, telemetry, evaluation, and more. Whether you're building an agent to automate tasks, assist users, or connect enterprise systems, MCP makes it possible. The AI Toolkit makes it easier! 📅 Save the Date! Don’t miss your chance to build alongside us and see MCP in action. This is more than just a demo, it’s a full walkthrough of what modern, production-aware AI development looks like. ✅ Register now to join the Learn Together livestream on the 5th August and take your first step toward building smarter AI applications. 👉 Click here to register for the event!How can I measure the quality of my agent's responses?
Welcome back to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a real question from the community with simple, practical guidance to help you build smarter agents. Today’s question comes from someone curious about measuring how well their agent responds: 💬Dear Agent Support My agent seems to be responding well—but I want to make sure I’m not just guessing. Ideally, I’d like a way to check how accurate or helpful its answers really are. How can I measure the quality of my agent’s responses? 🧠 What are Evaluations? Evaluations are how we move from “this feels good” to “this performs well.” They’re structured ways of checking how your agent is doing, based on specific goals you care about. At the simplest level, evaluations help answer: Did the agent actually answer the question? Was the output relevant and grounded in the right info? Was it easy to read or did it ramble? Did it use the tool it was supposed to? That might mean checking if the model pulled the correct file in a retrieval task. Or whether it used the right tool when multiple are registered. Or even something subjective like if the tone felt helpful or aligned with your brand. 🎯 Why Do We Do Evaluations? When you're building an agent, it’s easy to rely on instinct. You run a prompt, glance at the response, and think: “Yeah, that sounds right.” But what happens when you change a system prompt? Or upgrade the model? Or wire in a new tool? Without evaluations, there’s no way to know if things are getting better or quietly breaking. Evaluations help you: Catch regressions early: Maybe your new prompt is more detailed, but now the agent rambles. A structured evaluation can spot that before users do. Compare options: Trying out two different models? Testing a retrieval-augmented version vs. a base version? Evaluations give you a side-by-side look at which one performs better. Build trust in output quality: Whether you're handing this to a client, a customer, or just your future self, evaluations help you say, “Yes, I’ve tested this. Here’s how I know it works.” They also make debugging faster. If something’s off, a good evaluation setup helps you narrow down where it went wrong: Was the tool call incorrect? Was the retrieved content irrelevant? Did the prompt confuse the model? Ultimately, evaluations turn your agent into a system you can improve with intention, not guesswork. ⏳ When Should I Start Evaluating? Short answer: Sooner than you think! You don’t need a finished agent or a fancy framework to start evaluating. In fact, the earlier you begin, the easier it is to steer things in the right direction. Here’s a simple rule of thumb: If your agent is generating output, you can evaluate it. That could be: Manually checking if it answers the user’s question Spotting when it picks the wrong tool Comparing two prompt versions to see which sounds clearer Even informal checks can reveal big issues early before you’ve built too much around a flawed behavior. As your agent matures, you can add more structure: Create a small evaluation set with expected outputs Define categories you want to score (like fluency, groundedness, relevance) Run batch tests when you update a model Think of it like writing tests for code. You don’t wait until the end, you build them alongside your system. That way, every change gets feedback fast. The key is momentum. Start light, then layer on depth as you go. You’ll save yourself debugging pain down the line, and build an agent that gets better over time. 📊 AI Toolkit You don’t need a full evaluation pipeline or scoring rubric on day one. In fact, the best place to begin is with a simple gut check—run a few test prompts and decide whether you like the agent’s response. And if you don’t have a dataset handy, no worry! With the AI Toolkit, you can both generate datasets and keep track of your manual evaluations with the Agent Builder’s Evaluation feature. Sidebar: If you’re curious about deeper eval workflows, like using AI to assist in judging your agent output against a set of evaluators like fluency, relevance Tool Call, or even custom evaluators, we’ll cover that in a future edition of Agent Support. For now, let’s keep it simple! Here’s how to do it: Open the Agent Builder from the AI Toolkit panel in Visual Studio Code. Click the + New Agent button and provide a name for your agent. Select a Model for your agent. Within the System Prompt section, enter: You recommend a movie based on the user’s favorite genre. Within the User Prompt section, enter: What’s a good {{genre}} movie to watch? On the right side of the Agent Builder, select the Evaluation tab. Click the Generate Data icon (the first icon above the table). For the Rows of data to generate field, increase the total to 5. Click Generate. You’re now ready to start evaluating the agent responses! 🧪 Test Before You Build You can run the rows of data either individually or in bulk. I’d suggest starting with a single run to get an initial feel for how the feature works. When you click Run, the agent’s response will appear in the response column. Review the output. In the Manual Evaluation column, select either thumb up or thumb down. You can continue to run the other rows or even add your own row and pass in a value for {{city}}. Want to share the evaluation run and results with a colleague? Click the Export icon to save the run as a .JSONL file. You’ve just taken the first step toward building a more structured, reliable process for evaluating your agent’s responses! 🔁 Recap Here’s a quick rundown of what we covered: Evaluations help you measure quality and consistency in your agent’s responses. They’re useful for debugging, comparing, and iterating. Start early—even rough checks can guide better decisions. The AI Toolkit makes it easier to run and track evaluations right inside your workflow. 📺 Want to Go Deeper? Check out my previous live-stream for AgentHack: Evaluating Agents where I explore concepts and methodologies for evaluating generative AI applications. Although I focus on leveraging the Azure AI Evaluation SDK, it’s still an invaluable intro to learning more about evaluations. The Evaluate and Improve the Quality and Safety of your AI Applications lab from Microsoft Build 2025 provides a comprehensive self-guided introduction to getting started with evaluations. You’ll learn what each evaluator means, how to analyze the scores, and why observability matters—plus how to use telemetry data locally or in the cloud to assess and debug your app’s performance! 👉 Explore the lab: https://github.com/microsoft/BUILD25-LAB334/ And for all your general AI and AI agent questions, join us in the Azure AI Foundry Discord! You can find me hanging out there answering your questions about the AI Toolkit. I'm looking forward to chatting with you there! Whether you’re debugging a tool call, comparing prompt versions, or prepping for production, evaluations are how you turn responses from plausible to dependable.How do I control how my agent responds?
Welcome to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a question inspired by real conversations in the AI developer community, offering practical advice and tips. This time, we’re talking about one of the most misunderstood ingredients in agent behavior: the system prompt. Let’s dive in! 💬 Dear Agent Support I’ve written a few different prompts to guide my agent’s responses, but the output still misses the mark—sometimes it’s too vague, other times too detailed. What’s the best way to structure the instructions so the results are more consistent? Great question! It gets right to the heart of prompt engineering. When the output feels inconsistent, it’s often because the instructions aren’t doing enough to guide the model’s behavior. That’s where prompt engineering can make a difference. By refining how you frame the instructions, you can guide the model toward more reliable, purpose-driven output. 🧠 What Is Prompt Engineering (and Why It Matters for Agents) Before we can fix the prompt, let’s define the craft. Prompt engineering is the practice of designing clear, structured input instructions that guide a model toward the behavior you want. In agent systems, this usually means writing the system prompt, a behind-the-scenes instruction that sets the tone, context, and boundaries for how the agent should act. While prompt engineering feels new, it’s rooted in decades of interface design, instruction tuning, and human-computer interaction research. The big shift? With large language models (LLMs), language becomes the interface. The better your instructions, the better your outcomes. 🧩 The Anatomy of a Good System Prompt Think of your system prompt as a blueprint for how the agent should operate. It sets the stage before the conversation starts. A strong system prompt should: Define the role: Who is this agent? What’s their tone, expertise, or purpose? Clarify the goal: What task should the agent help with? What should it avoid? Establish boundaries: Are there any constraints? Should it cite sources? Stay concise? Here’s a rough template you can build from: “You are a helpful assistant that specializes in [domain]. Your job is to [task]. Keep responses [format/length/tone]. If you’re unsure, respond with ‘I don’t know’ instead of guessing.” 🛠️ Why Prompts Fail (Even When They Sound Fine) Common issues we see: Too vague (“Be helpful” isn’t helpful.) Overloaded with logic (Treating the system prompt like a config file.) Conflicting instructions (“Be friendly” + “Use legal terminology precisely.”) Even well-written prompts can underperform if they’re mismatched with the model or task. That’s why we recommend testing and refining early and often! ✏️ Skip the Struggle— let the AI Toolkit Write It! Writing a great system prompt takes practice. And even then, it’s easy to overthink it! If you’re not sure where to start (or just want to speed things up), the AI Toolkit provides a built-in way to generate a system prompt for you. All you have to do is describe what the agent needs to do, and the AI Toolkit will generate a well-defined and detailed system prompt for your agent. Here's how to do it: Open the Agent Builder from the AI Toolkit panel in Visual Studio Code. Click the + New Agent button and provide a name for your agent. Select a Model for your agent. In the Prompts section, click Generate system prompt. In the Generate a prompt window that appears, provide basic details about your task and click Generate. After the AI Toolkit generates your agent’s system prompt, it’ll appear in the System prompt field. I recommend reviewing the system prompt and modifying any parts that may need revision! Heads up: System prompts aren’t just behind-the-scenes setup, they’re submitted along with the user prompt every time you send a request. That means they count toward your total token limit, so longer prompts can impact both cost and response length. 🧪 Test Before You Build Once you’ve written (or generated) a system prompt, don’t skip straight to wiring it into your agent. It’s worth testing how the model responds with the prompt in place first. You can do that right in the Agent Builder. Just submit a test prompt in the User Prompt field, click Run, and the model will generate a response using the system prompt behind the scenes. This gives you a quick read on whether the behavior aligns with your expectations before you start building around it. 🔁 Recap Here’s a quick rundown of what we covered: Prompt engineering helps guide your agent’s behavior through language. A good system prompt sets the tone, purpose, and guardrails for the agent. Test, tweak, and simplify—especially if responses seem inconsistent or off-target. You can use the Generate system prompt feature within the AI Toolkit to quickly generate instructions for your agent. 📺 Want to Go Deeper? Check out my latest video on how to define your agent’s behavior—it’s part of the Build an Agent Series, where I walk through the building blocks of turning an idea into a working AI agent. The Prompt Engineering Fundamentals chapter from our aka.ms/AITKGenAI curriculum overs all the essentials—prompt structure, common patterns, and ways to test and improve your outputs. It also includes exercises so you can get some hands-on practice. 👉 Explore the full curriculum: aka.ms/AITKGenAI And for all your general AI and AI agent questions, join us in the Azure AI Foundry Discord! You can find me hanging out there answering your questions about the AI Toolkit. I'm looking forward to chatting with you there! And remember, great agent behavior starts with great instructions—and now you’ve got the tools to write them.
