Microsoft Agent Framework for Agentic AI: Everything You Need to Know
- Ganesh Sharma
- 2 days ago
- 10 min read
Microsoft's agentic AI story used to be split across two separate projects, AutoGen for multi-agent experimentation and Semantic Kernel for enterprise grade orchestration. Microsoft Agent Framework brings those two lineages together into one framework, built by the same teams, aimed specifically at teams taking agents from prototype to production.
This blog explains what Microsoft Agent Framework is, how it fits into agentic AI development, how implementation generally works, and how it compares to other frameworks used for building agents.
Microsoft Agent Framework
Merging AutoGen and Semantic Kernel Into One Framework
Microsoft Agent Framework, often shortened to MAF, is an open, multi-language framework for building production grade AI agents and multi-agent workflows in Python and .NET. It combines AutoGen's simple agent abstractions for single and multi-agent patterns with Semantic Kernel's enterprise features, such as session based state management, type safety, and telemetry.
What Does It Add Beyond the Two Frameworks It Replaces?
Beyond merging AutoGen and Semantic Kernel, Microsoft Agent Framework introduces graph based workflows that give developers explicit control over multi-agent execution paths, along with a more robust state management system built for long running and human-in-the-loop scenarios.
Is This the Right Framework for Your Use Case?
According to Microsoft's own guidance, the framework is a strong fit for teams building agents they expect to run in production, that need orchestration beyond a single prompt or stateless chat loop, that want graph based patterns such as sequential, concurrent, handoff, and group collaboration, and that need provider flexibility so their architecture can evolve without major rewrites.
Agent Structure and Behavior
The framework brings together four main areas: agents that use language models to process input and call tools, an opinionated harness agent for long, multi-step tasks, graph based workflows that connect agents through explicit execution paths, and integrations with model providers and other tooling.
Harness Agent for Long-Running Tasks
The harness agent is an opinionated, batteries included agent designed for long, multi-step tasks, offering planning and to-do tracking, context compaction, file access and memory, tool approval controls, and observability, without requiring a developer to build that structure manually.
When Should You Use an Agent Versus a Workflow?
Microsoft's own guidance suggests using a single agent when a task is open ended or conversational, when autonomous tool use and planning are enough, or when a single language model call with tools suffices. A workflow becomes the better choice when the process has well defined steps, when execution order needs explicit control, or when multiple agents or functions need to coordinate together.
Is Microsoft Agent Framework Right for Your Agentic AI Project?
Microsoft Agent Framework tends to be a strong fit for teams that are moving agents from prototype into production and need durability, observability, governance, or human-in-the-loop control along the way.
The framework itself is open source and free to use, released under the MIT license. Costs come from the underlying language model provider being used, whether that is Microsoft Foundry, Azure OpenAI, OpenAI, or another supported provider, along with any Azure infrastructure used for hosting.
Whether Microsoft Agent Framework is the right choice depends on how production focused a project already is. For teams building toward real deployment with governance and observability requirements, the framework offers substantial built in support. For lightweight prototypes or teams not yet invested in the Microsoft ecosystem, a simpler or more provider agnostic framework might involve less initial setup.
Working With Microsoft Agent Framework
Installing the Framework
The framework is installed as a package, using pip for Python or as a NuGet package for .NET, giving access to its core agent and workflow components.
Connecting a Model Provider
Agents are connected to a model provider such as Microsoft Foundry, Azure OpenAI, OpenAI, or Ollama, with the framework designed to support additional providers being added over time.
Defining an Agent's Instructions and Tools
Each agent is configured with instructions describing its behavior, along with any tools or MCP servers it should have access to for taking action beyond generating text.
Choosing Between an Agent and a Workflow
For simpler, open ended tasks, a single agent configuration is enough. For more complex processes, developers define a workflow using graph based patterns such as sequential, concurrent, handoff, or group collaboration to control how multiple agents or functions interact.
How Does a Request Move Through a Workflow?
A request enters the workflow at a defined starting point, moves through connected agents and functions according to the workflow's execution paths, and can include checkpointing, streaming, or human-in-the-loop steps before reaching a final result.
Actual implementation details vary depending on the chosen model provider, the complexity of the workflow, and whether the deployment uses Foundry hosted infrastructure or a self managed setup.
Advantages and Limitations of Microsoft Agent Framework
Strengths of Microsoft Agent Framework
Advantage | Details |
Combines two proven lineages | Brings together AutoGen's agent abstractions and Semantic Kernel's enterprise features in one framework. |
Graph based workflows | Supports sequential, concurrent, handoff, and group collaboration patterns with checkpointing and human-in-the-loop support. |
Built in observability | Native OpenTelemetry integration supports distributed tracing, monitoring, and debugging out of the box. |
Provider flexibility | Supports Microsoft Foundry, Azure OpenAI, OpenAI, Ollama, and other providers, with more added over time. |
Open source and free | Released under the MIT license with no cost for using the framework itself. |
What Are the Trade-Offs of Using Microsoft Agent Framework?
Limitation | Details |
Newer than its predecessors | As a direct successor to AutoGen and Semantic Kernel, it has a shorter independent track record than either project on its own. |
Migration effort for existing projects | Teams already using AutoGen or Semantic Kernel need to follow a migration path to move to the new framework. |
Strongest within the Microsoft ecosystem | While provider flexible, the deepest integration and hosting benefits are tied to Microsoft Foundry and Azure. |
Third-party system risk sits with the developer | Microsoft's own documentation notes that using non-Microsoft models or servers carries usage and cost responsibility for the developer. |
What Does Microsoft Agent Framework Cost to Use?
The core Microsoft Agent Framework is open source and free to use under the MIT license. Costs come from the underlying language model provider connected to the agents, such as Azure OpenAI or OpenAI token usage, along with any Azure infrastructure costs if agents are deployed to Foundry hosted infrastructure rather than run locally or self hosted.
Microsoft Agent Framework Compared to Other Agentic AI Frameworks
Microsoft Agent Framework is one of several frameworks available for building agentic AI systems, and its position as the direct successor to both AutoGen and Semantic Kernel is what sets it apart from frameworks built from a single lineage.
Microsoft Agent Framework and LangGraph
LangGraph represents agent logic as an explicit graph of nodes and edges, focused specifically on state and branching control. Microsoft Agent Framework also offers graph based workflows, but pairs them with a built in harness agent, native observability, and enterprise features inherited from Semantic Kernel, giving it a broader production oriented feature set out of the box.
Microsoft Agent Framework and CrewAI
CrewAI organizes agents around defined roles and tasks for team-style collaboration. Microsoft Agent Framework instead offers a mix of workflow patterns, including handoff and group collaboration, along with enterprise grade session state and observability that CrewAI does not provide natively.
Microsoft Agent Framework and AutoGen
AutoGen is the predecessor Microsoft Agent Framework was built to replace, now in maintenance mode. The new framework carries forward AutoGen's simple agent abstractions while adding Semantic Kernel's enterprise features and new graph based workflows, making it the recommended path for new projects going forward.
Microsoft Agent Framework and the OpenAI Agents SDK
The OpenAI Agents SDK offers a lightweight handoff mechanism built specifically around OpenAI's models. Microsoft Agent Framework is provider flexible, supporting OpenAI alongside Microsoft Foundry, Azure OpenAI, and other providers, while also offering deeper enterprise features such as session state management and built in observability.
Microsoft Agent Framework and Claude Agent SDK
Anthropic's Claude Agent SDK is built specifically around Claude models and the harness that powers Claude Code. Microsoft Agent Framework is provider flexible, supporting Anthropic's models alongside several others, and places more emphasis on graph based multi-agent workflows and enterprise production tooling.
Microsoft Agent Framework and Google ADK
Google's Agent Development Kit emphasizes hierarchical multi-agent structures and strong debugging tools, proven inside Google's own products. Microsoft Agent Framework takes a graph based workflow approach instead of a strict hierarchy, with its own strengths in enterprise state management and observability inherited from Semantic Kernel.
Microsoft Agent Framework and LlamaIndex Agents
LlamaIndex Agents are built around a strong data and retrieval foundation, making them a natural fit for document and data heavy agentic tasks. Microsoft Agent Framework is more general purpose, with production oriented workflow and observability features that apply across a broader range of task types beyond retrieval centric work.
Microsoft Agent Framework and Haystack
Haystack, from deepset, carries a strong heritage in search and retrieval pipelines with agent capabilities layered on top. Microsoft Agent Framework is a more general purpose orchestration framework, with its enterprise features oriented toward production deployment across a wide range of agentic task types, not specifically retrieval.
Microsoft Agent Framework and Rasa
Rasa's Agentic AI is purpose built for conversational, customer facing agents using structured flows and guard conditions. Microsoft Agent Framework takes a broader, general purpose approach to agent and workflow orchestration, making it suited to a wider range of tasks beyond conversational dialogue management specifically.
Which Teams Get the Most Out of Microsoft Agent Framework?
Microsoft Agent Framework tends to be the right choice when a team wants to:
Move agents from prototype into production with durability, observability, and governance built in
Use graph based workflow patterns such as sequential, concurrent, handoff, and group collaboration
Take advantage of a harness agent for long, multi-step tasks without building that structure manually
Maintain provider flexibility across Microsoft Foundry, Azure OpenAI, OpenAI, and other supported providers
Migrate an existing AutoGen or Semantic Kernel project to an actively developed, unified framework
Does Microsoft Agent Framework Improve Agent Reliability?
The framework itself does not generate responses, but its built in observability, checkpointing, and human-in-the-loop support directly influence how reliably a multi-agent workflow can be monitored, debugged, and corrected when something goes wrong.
Native OpenTelemetry integration and session based state management give teams strong visibility into how an agent or workflow behaved during a run. Reliability still depends on how well each agent's instructions, tools, and workflow structure are designed, not the framework's tooling alone.
How CodersArts Works With Microsoft Agent Framework
We use Microsoft Agent Framework when building agentic AI systems that need to move from prototype into production with strong observability, governance, and human-in-the-loop control, particularly for clients already invested in the Microsoft or Azure ecosystem. This includes designing workflow patterns, configuring the harness agent for long running tasks, and setting up telemetry for monitoring agent behavior in production.
Our experience with Microsoft Agent Framework includes projects such as migrating existing AutoGen based systems to the new framework, building enterprise workflows that require checkpointing and human-in-the-loop approval steps, and multi-agent systems deployed through Microsoft Foundry hosted infrastructure. This experience helps clients determine when Microsoft Agent Framework's production oriented feature set is the right fit for their agentic AI needs.
Frequently Asked Questions
Is Microsoft Agent Framework Free to Use?
Yes. Microsoft Agent Framework is open source under the MIT license. Costs come from the underlying language model provider and any Azure infrastructure used for hosting, not from the framework itself.
How Is Microsoft Agent Framework Different From AutoGen?
Microsoft Agent Framework is the direct successor to AutoGen, carrying forward its agent abstractions while adding Semantic Kernel's enterprise features and new graph based workflows. AutoGen itself is now in maintenance mode, with Microsoft directing new development toward Agent Framework.
Why Do Teams Choose Microsoft Agent Framework for Agentic AI Projects?
Teams often choose Microsoft Agent Framework when they need to move agents into production with built in observability, governance, and durability, or when they are migrating an existing AutoGen or Semantic Kernel project to an actively developed framework.
Can Microsoft Agent Framework Be Used for Applications Besides Agentic AI?
Microsoft Agent Framework is built primarily for agent and multi-agent workflow applications, though its underlying components, such as middleware and provider integrations, can also support broader language model application development.
Do I Need Microsoft Agent Framework to Build an Agentic AI Application?
No. Microsoft Agent Framework is one of several frameworks available for building agents. Alternatives such as LangGraph, CrewAI, the OpenAI Agents SDK, Claude Agent SDK, Google ADK, LlamaIndex Agents, Haystack, and Rasa can also serve this purpose, depending on the specific requirements of the project.
What Is Required to Migrate From AutoGen or Semantic Kernel?
Microsoft provides dedicated migration guides for both AutoGen and Semantic Kernel projects, generally involving mapping existing agent and orchestration concepts onto Microsoft Agent Framework's agent, harness, and workflow abstractions before adopting its provider integrations.
What Should Teams Evaluate Before Using Microsoft Agent Framework for Agentic AI?
Teams should consider how production ready their agentic system needs to be, whether they are already invested in the Microsoft or Azure ecosystem, how much they value built in observability and governance features, and whether migrating from an existing AutoGen or Semantic Kernel project makes sense for their timeline.
What Services Does CodersArts Offer?
Beyond agentic AI and RAG specific delivery and partnership work, CodersArts offers a wider range of services that agencies, businesses, and individual developers regularly rely on, whether as part of a partnership or on their own.
Agentic AI and RAG Development
Custom agentic AI and RAG development, starting from proof of concept through to full production builds, along with broader LLM and generative AI development for businesses building AI-powered products and internal tools.
Consultation
Project consultation for businesses and agencies evaluating an agentic AI or RAG initiative, helping assess feasibility, recommend the right technical approach, and scope a project before committing to full development.
One-on-One Mentorship
Personalized, expert-led mentorship for developers and teams looking to build hands-on agentic AI, RAG, machine learning, or AI engineering skills, with guidance tailored to individual or team goals and current experience level.
Dedicated Team and Team Augmentation
Dedicated AI engineering teams, or engineers who work as an extension of an existing in-house or agency team, scaling up or down based on project needs.
Ongoing Support and Maintenance
Post-launch monitoring, optimization, and maintenance for agentic AI and RAG systems already in production, helping ensure performance and reliability do not degrade over time.
Job Support Services
Remote job support for developers and engineers working on live agentic AI, LLM, or RAG projects, including pair programming, code reviews, agent workflow setup, debugging, and help meeting sprint deadlines under expert guidance.
Corporate and Team Training
Structured training and workshops for teams looking to build internal agentic AI and RAG capability, covering hands-on implementation as well as best practices for evaluation and production readiness.
White-Label and Partnership Delivery
CodersArts also partners with agencies, consultancies, and technology companies to deliver agentic AI and RAG development on their behalf, whether white-label, co-branded, or embedded alongside an existing team.
Whether you are an agency looking for a delivery partner, a business exploring your first agentic AI project, or a developer seeking hands-on mentorship, CodersArts offers services to support your AI development journey.
Reach out at contact@codersarts.com or visit www.codersarts.com to discuss your agentic AI project.
Continue Exploring Microsoft Agent Framework and Agentic AI Resources
If you found this blog helpful, explore more agentic AI, RAG, and enterprise AI resources from CodersArts AI to see how organizations are applying these systems to real world applications.
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