GRAPHIC APPAREL SHOPThe ability to orchestrate multiple AI agents working together on complex tasks has become a critical capability for enterprise applications. Microsoft's Agent Framework is the direct successor to two pioneering projects, Semantic Kernel and AutoGen, combining their strengths while introducing powerful new capabilities.
This open-source framework is currently in public preview and represents Microsoft's vision for building, orchestrating, and deploying AI agents with support for both Python and .NET, bringing enterprise-grade features to the multi-agent development landscape.
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A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.Bridging the Gap Between Simple Agents and Complex Workflows
As organizations rush to adopt AI agents, they quickly discover that building a single chatbot is one thing, but orchestrating multiple specialized agents to handle complex, multi-step business processes is an entirely different challenge.
Traditional approaches force developers to choose between simple, ad-hoc agent implementations and complex, hard-to-maintain orchestration systems. The lack of type safety leads to runtime errors that are difficult to debug, while state management across long-running conversations becomes a nightmare.
When you add requirements like human-in-the-loop approval processes, checkpointing for recovery, and integration with various LLM providers, the complexity multiplies exponentially.
Enter the Microsoft Agent Framework, which confronts these challenges by providing a unified platform that scales from simple single-agent scenarios to sophisticated multi-agent workflows.
The framework recognizes that modern AI applications need both the flexibility of autonomous agents and the reliability of explicitly defined workflows, and it delivers both through a thoughtfully designed architecture.
Why I Like It
What sets the Agent Framework apart is its pragmatic approach to multi-agent development. Rather than forcing you to abandon your existing work with Semantic Kernel or AutoGen, Microsoft provides clear migration guides that respect your investment in these platforms.
The framework's dual-language support, with consistent APIs across Python and .NET, means teams can work in their preferred ecosystem without sacrificing functionality. The inclusion of a DevUI package for interactive development and debugging shows Microsoft's commitment to developer experience, something often overlooked in AI frameworks.
Key Features and Functionality
The Agent Framework is built around two primary categories of capabilities that work harmoniously together. First, individual AI agents that leverage large language models to process inputs, make decisions, call tools, and generate responses.
These agents can be augmented with threads for multi-turn conversations, context providers for memory, and middleware for custom processing pipelines. The framework supports multiple LLM providers out of the box, including Azure OpenAI, OpenAI, and Azure AI Foundry, giving you flexibility in your model choices.
The second category introduces workflows, a game-changing approach to multi-agent orchestration. These graph-based workflows connect agents and deterministic functions through explicit data flows, supporting streaming, checkpointing, time-travel debugging, and human-in-the-loop interactions.
The framework provides built-in orchestration patterns including sequential execution, concurrent processing, agent handoffs, and the innovative Magentic pattern. This is where the framework truly shines, offering developers explicit control over multi-agent execution paths while maintaining the flexibility to adapt to dynamic scenarios.
Creating a basic agent is remarkably straightforward. Here's a Python example that demonstrates the clean API design:
import asyncio
from agent_framework import ChatAgent
from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential
async def main():
# Create an agent with Azure OpenAI
agent = ChatAgent(
chat_client=AzureOpenAIChatClient(
endpoint="https://your-endpoint.openai.azure.com/",
deployment_name="gpt-4o-mini",
api_version="2025-03-01-preview",
credential=AzureCliCredential()
),
instructions="You are a helpful research assistant specialized in AI."
)
result = await agent.run("Explain multi-agent orchestration")
print(result)
asyncio.run(main()) The .NET implementation mirrors this simplicity while leveraging the platform's strong typing and async patterns. What's particularly elegant is how tools and functions integrate seamlessly into agents. The framework supports native functions in both Python and C#, Agent-to-Agent (A2A) communication, and the Model Context Protocol for standardized tool integration.
Under the Hood: Architecture and Implementation
The repository structure reveals a well-organized, production-ready codebase. The Python packages directory contains modular components including the core framework, Azure AI integration, Copilot Studio support, and experimental features in the lab package. The .NET source follows a similar structure with parallel implementations ensuring feature parity across languages.
At its core, the framework uses a graph-based execution model for workflows. Executors represent processing units (agents or functions), while edges define message flows with optional conditions.
This architecture enables powerful features like type-safe message routing, where the framework validates that messages flowing between components match expected types, preventing a whole class of runtime errors that plague traditional agent systems. The StepContext and DeliveryMapping classes handle message routing and state management, ensuring that workflows can checkpoint their state and resume from failure points.
The framework's middleware system provides hooks for intercepting and modifying agent behavior at various stages. This enables cross-cutting concerns like logging, telemetry, content moderation, and custom validation without cluttering agent logic. The built-in OpenTelemetry integration means you get distributed tracing out of the box, critical for debugging complex multi-agent interactions in production.
Python developers benefit from async-first design using asyncio, while .NET developers get Task-based async patterns with full support for cancellation tokens and structured concurrency. Both implementations share a common conceptual model, making it easy for teams to collaborate across language boundaries or migrate between platforms as needs evolve.
Real-World Use Cases and Applications
The Agent Framework excels in scenarios requiring sophisticated agent coordination. Consider a content creation pipeline where a Writer agent generates initial drafts, a Reviewer agent provides feedback, and an Editor agent finalizes the content. The framework's sequential orchestration pattern makes this straightforward to implement, with full visibility into each stage's progress and the ability to checkpoint between steps.
In customer support applications, the framework enables dynamic agent routing where a Triage agent classifies inquiries, then hands off to specialized agents for Technical Support, Billing, or Product Questions. The handoff pattern maintains context across agents while allowing each to focus on its domain expertise. Human-in-the-loop capabilities let supervisors intervene when agents request approval or encounter uncertainty.
Research and analysis workflows benefit from the concurrent execution pattern. Multiple Research agents can simultaneously query different data sources, academic databases, or APIs, with a Synthesis agent combining their findings into coherent reports. The framework's checkpointing ensures long-running research tasks survive interruptions, while time-travel debugging helps developers understand exactly how conclusions were reached.
Financial services organizations are using the framework for compliance and risk assessment, where multiple specialized agents analyze different aspects of transactions or loan applications, with a Coordinator agent making final decisions based on their collective input. The framework's built-in observability helps meet audit requirements by providing complete traces of agent interactions and decisions.
Community and Contribution
The Microsoft Agent Framework benefits from being developed by the same teams behind Semantic Kernel and AutoGen, bringing years of experience in AI agent development to bear. The contribution guidelines are clear and welcoming, emphasizing the importance of discussion before major changes and providing specific guidance for both Python and .NET development.
The repository shows healthy activity with over 210 open issues covering feature requests, bug reports, and community discussions. Recent issues reveal active development on features like Computer Use integration, improved coding assistant support, and enhanced content type handling. The team's responsiveness and the presence of experimental features in the AF Labs package demonstrate a commitment to innovation while maintaining stability in core components.
Microsoft maintains comprehensive documentation on Microsoft Learn, including tutorials, user guides, and architectural decision records in the repository. The availability of extensive workflow samples and getting started examples lowers the barrier to entry significantly.
Usage Rights and License Terms
The Microsoft Agent Framework is released under the MIT License, one of the most permissive open-source licenses available. This grants you the freedom to use, copy, modify, merge, publish, distribute, sublicense, and sell copies of the software without restriction. The only requirements are including the copyright notice and license text in substantial portions of the software.
The MIT License means you can integrate the Agent Framework into commercial products, create derivative works, or modify it for your specific needs without needing to open-source your own code. This permissive licensing aligns with Microsoft's broader open-source strategy and ensures the framework can be adopted widely across different organizational contexts, from startups to Fortune 500 companies.
While the framework itself is freely usable, remember that LLM providers like Azure OpenAI and OpenAI have their own terms of service and pricing. The framework's transparency FAQ addresses important considerations around data handling and third-party integrations, emphasizing that developers using the framework with external agents or servers do so at their own risk and should review data sharing practices carefully.
Impact and Future Potential
The Agent Framework represents a significant consolidation in Microsoft's AI agent strategy. Rather than maintaining separate projects with overlapping functionality, the company is channeling its efforts into a unified platform that combines the best aspects of Semantic Kernel's enterprise features with AutoGen's multi-agent orchestration patterns. This consolidation reduces fragmentation in the ecosystem and gives developers a clear path forward.
The framework's impact extends beyond Microsoft's own ecosystem. By supporting the Model Context Protocol and providing extensibility through A2A communication, the framework positions itself as a potential industry standard for agent orchestration. While the dual-language approach breaks down barriers between Python-centric AI research communities and .NET enterprise developers, fostering collaboration and knowledge sharing.
Looking ahead, the framework's architecture is well-positioned to incorporate emerging AI capabilities. The lab packages serve as an incubator for experimental features like reinforcement learning and advanced benchmarking.
As large language models continue to improve and new modalities emerge, the framework's abstraction layers and extensibility points provide a stable foundation for integrating these advances without requiring application rewrites.
The emphasis on responsible AI, evident in the comprehensive Transparency FAQ, sets important precedents for the industry. By openly discussing limitations, data handling concerns, and mitigation strategies, Microsoft encourages developers to think critically about the ethical implications of their agent implementations. This responsible approach will become increasingly important as AI agents take on more autonomous decision-making roles.
About Microsoft
Microsoft Corporation stands as one of the world's leading technology companies, with a mission to empower every person and every organization on the planet to achieve more. Founded in 1975, Microsoft has evolved from its origins in personal computing to become a driving force in cloud computing, artificial intelligence, and enterprise software. The company's Azure cloud platform serves as the backbone for countless AI and machine learning applications worldwide, while products like Microsoft 365 and Teams reshape how billions of people work and collaborate.
Microsoft's commitment to open source has grown significantly in recent years, with the company becoming one of the largest contributors to open-source projects globally. This shift reflects a recognition that innovation thrives in open ecosystems where developers worldwide can contribute, collaborate, and build upon shared foundations. The Agent Framework exemplifies this philosophy, providing an open platform that benefits from community input while advancing Microsoft's own AI capabilities.
Through initiatives like Microsoft Copilot, the company is bringing AI assistance to everyday tasks across its product portfolio. The Agent Framework aligns with this vision, providing the infrastructure that developers need to create the next generation of AI-powered applications. Microsoft's scale and reach mean that innovations developed in the Agent Framework can quickly propagate to millions of users through integrations with Azure, Microsoft 365, and other platforms.
Conclusion: A Foundation for the Agent Era
The Microsoft Agent Framework arrives at a pivotal moment in AI development, when the industry is moving beyond simple chatbots toward sophisticated multi-agent systems that can handle complex, real-world tasks. By unifying the best ideas from Semantic Kernel and AutoGen, adding powerful workflow orchestration, and maintaining an unwavering focus on developer experience, Microsoft has created a framework that balances flexibility with reliability, simplicity with power.
Whether you're building a simple customer support agent or orchestrating a complex research pipeline with dozens of specialized agents, the framework provides the tools you need without unnecessary complexity. The comprehensive documentation, active community, and Microsoft's backing give confidence that this is a platform worth investing in for the long term.
For developers ready to explore the future of AI agent development, the Microsoft Agent Framework offers a compelling starting point. Visit the GitHub repository to get started, explore the official documentation, and join the conversation on Discord. The agent era is here, and Microsoft has provided the framework to help you build it.
Microsoft Agent Framework: Evolving AI Agent Development