Microsoft Agent Framework
Agents aren’t new, they’ve been evolving in labs and repos for years. What’s different now is a framework that actually pulls the pieces together: the Microsoft Agent Framework. Rather than another prototype, it builds on lessons from Semantic Kernel and AutoGen to offer a consistent, enterprise-ready foundation. It tackles the practical gaps developers faced: security, tool integration, state management, and reliable orchestration and fits naturally into the Azure ecosystem so teams can build, operate, and scale multi-agent systems with fewer workarounds.
History & Evolution
Where we started: Azure OpenAI SDK
Before we talk about agent framework, we need to understand where we came from. When Azure OpenAI was first released, most of us were working directly with the SDK and we were writing prompts. We used to send them to the model and get back the plain text information. It was simple, but if you wanted to build something serious like calling APIs or maintaining memory or coordinating multiple steps, you had to create a custom fold for it. There was no structured way to build real applications.
Semantic Kernel: Structure, Strengths, and Limitations
Then came the semantic kernel. Semantic kernel was Microsoft’s first real attempt to make this more structured. It introduced the skills and functions so you could wrap the APIs or code as reusable functions. Semantic kernel had planners so the model could figure out which functions to call. Also, it supported the memory connectors which made it possible to store embeddings and perform retrievals. This was a big improvement with semantic kernel. You could build more modular functions or systems, but it had some limitations also. The planning system wasn’t always reliable for the multi-step scenario, especially with complex tasks, and the middleware and security were not strong enough. Once you went beyond a single agent, it became messy very quickly.
AutoGen: Multi-Agent Collaboration and Its Gaps
Then we saw AutoGen, which gained popularity rapidly. AutoGen was amazing because it introduced multi-agent collaboration. You could define multiple agents, give them different roles, and they could talk to each other to solve tasks together. But the issue was that AutoGen was community driven. It wasn’t fully aligned with Azure’s enterprise ecosystem. It lacked strong standards for memory tools or security, and MCP integration wasn’t there. Observability was minimal. Enterprises found it difficult to use securely at scale.
Introducing the Microsoft Agent Framework
That brings us to Microsoft agent framework. The agent framework is the next step in this evolution. It combines the simplicity of the SDK, the structured approach of semantic kernel, and the flexibility of AutoGen, but in a clean, unified, and enterprise-ready framework. Agent framework introduces proper agent abstractions and tool integration, thread-based state, middleware hooks, and workflows with executors and edges, even MCP for external tools and observability all in one place. Think of it as Microsoft 's way of saying this is how, moving forward, you have to build agentic systems.
Microsoft agent framework is a developer framework for building, orchestrating, and managing intelligent agents in a structured, scalable, and secure way. With it, you can create intelligent agents that can reason and act, attach different tools and functions that they can call, maintain conversation state using threads, connect to enterprise data sources securely using MCP, and even build workflows to orchestrate multiple agents. You can insert middleware for guardrails, logging, and validation, and then observe, debug, and manage all these flows in the production environment. It fits naturally in the Azure AI ecosystem where you work with Azure OpenAI, Azure AI services, databases, APIs, and other Azure services.
Core Components
Let’s get familiar with the core components of this framework.
Agents
Agents are at the heart of the framework. You can use built-in agents like chat client agents for quick setups or write custom agents for more control. Agents can run in non-streaming mode, which gives the complete response at once, suitable for backend use, or streaming mode, where you receive partial tokens as they are generated, perfect for chat or voice bots. Agents can produce structured output like JSON, useful for predictable downstream processing or chaining with other systems, such as defining a date and time format for calendar booking in Outlook 365.
Recommended by LinkedIn
Threads
Threads represent the conversation context. Every agent run happens within a thread. If you use the same thread ID for multiple runs, the agent retains previous messages and can continue the conversation naturally. This enables multi-turn conversation and persistent memory across sessions.
Tools
Tools turn an agent from a chatbot into something truly useful. Agents can call attached functions either at creation or dynamically at runtime. The model decides when to call a tool based on its schema or context. You can secure tools using allow-lists so the agent can only access approved functions, which is crucial for enterprise security.
Middleware
Middleware provides clean interception points around turns, function calls, or model interactions. There are three types: run middleware, which wraps the entire agent run (useful for logging or authentication); function-calling middleware, which wraps tool calls (ideal for validation and security); and chat client middleware, which intercepts model calls (great for tracing and transformation). Instead of adding features into agents directly, you can plug them cleanly into middleware for easier maintenance.
Workflows
Workflows, explored in detail in the third video, act like a graph of steps. Each step is an executor, and connections between them are called edges. You can design sequential, branching, or parallel flows. This solves orchestration challenges that semantic kernel planners had, making multi-agent collaboration clean and manageable.
MCP - Model Context Protocol
MCP or model context protocol, allows agents to connect to external MCP servers and discover tools dynamically. Instead of hardcoding SQL connectors or APIs, you can expose databases through MCP so the agent can query them securely at runtime. This is crucial for enterprise use because multiple teams can connect different agents to a single MCP server without direct database connections.
Agents, threads, tools, middleware, workflows, and MCP together make Microsoft agent framework not just another library but a unified way to build, structure, secure, and scale agentic systems. Using Microsoft agent framework, you can create agents in Azure AI agent service, attach tools, perform function calling, and even connect external knowledge sources.
Why It Matters
The Microsoft Agent Framework isn’t just another library, it’s a unified foundation for building intelligent, secure, and scalable agentic systems that fit seamlessly into the Azure AI ecosystem.
With it, developers can:
