Semantic Kernel vs Microsoft Agent Framework: Which Should You Actually Use?

Microsoft’s AI agent ecosystem has moved quickly.

For a while, the decision looked complicated. Semantic Kernel was the practical enterprise SDK. AutoGen was the experimental multi-agent research framework. Then Microsoft introduced Microsoft Agent Framework to bring those worlds together.

That leaves developers with an obvious question:

Should you use Semantic Kernel or Microsoft Agent Framework for a new project?

As of 2026, the short answer is: use Microsoft Agent Framework for new agent applications unless you have a clear reason not to. Semantic Kernel still matters, especially for existing applications and some non-agent scenarios, but Microsoft Agent Framework is now the main path forward for agent development.

The Short Answer

If you are starting a new AI agent project today, choose Microsoft Agent Framework.

If you already have a production Semantic Kernel application, do not panic. You do not need to rewrite everything immediately. But for new agent work, new orchestration work, and new long-running multi-step AI workflows, Microsoft Agent Framework is the direction Microsoft is investing in.

New agent app?
  Use Microsoft Agent Framework

Existing Semantic Kernel app?
  Keep it stable, then migrate deliberately

Simple LLM call?
  You may not need either framework

That last line matters. Not every AI feature needs an agent framework. If your application only needs one prompt, one model call, or one predictable transformation, a direct model client may be simpler.

What Is Semantic Kernel?

Semantic Kernel is Microsoft’s SDK for integrating large language models, prompts, plugins, memory, connectors, and orchestration patterns into applications.

It became popular because it gave enterprise teams a structured way to build AI-powered software without hand-rolling every prompt, connector, and tool integration. Semantic Kernel supports .NET, Python, and Java, and it has been used for production-grade AI applications across the Microsoft ecosystem.

Semantic Kernel includes concepts such as:

• Kernels
• Plugins
• Prompts
• Planners
• Memory connectors
• Filters
• Observability
• Agent and process frameworks

Microsoft’s Semantic Kernel documentation still describes it as a framework for building robust AI solutions with plugins, memory, observability, filters, and related enterprise components.

In practical terms, Semantic Kernel was the “serious app integration” option: useful when you wanted AI features inside a real application rather than a one-off chatbot prototype.

What Is Microsoft Agent Framework?

Microsoft Agent Framework is Microsoft’s newer open-source SDK and runtime for building AI agents and multi-agent workflows.

It combines ideas from Semantic Kernel and AutoGen into one framework. Microsoft’s own Agent Framework overview describes it as the direct successor to both Semantic Kernel and AutoGen for agent development.

The framework includes three major capability areas:

• Agents: LLM-backed agents that can process messages, use tools, call MCP servers, and generate responses
• Harness: a batteries-included agent loop for long-running tasks, planning, memory, file access, approvals, and observability
• Workflows: graph-based workflows that connect agents and functions with type-safe routing, checkpointing, and human-in-the-loop support

Microsoft announced Microsoft Agent Framework version 1.0 in April 2026 for .NET and Python, positioning it as production-ready with stable APIs and long-term support.

That changed the decision. Before 1.0, Semantic Kernel was the safer production choice for many teams. After Agent Framework 1.0, the recommendation is much clearer for new agent work.

Why Microsoft Created Agent Framework

Semantic Kernel and AutoGen solved different problems.

Semantic Kernel was more enterprise-oriented. It provided integration patterns, model connectors, memory abstractions, filters, telemetry, and the kind of structure teams need for production applications.

AutoGen was more experimental and research-driven. It explored multi-agent collaboration, group chat, handoff patterns, and autonomous workflows.

Microsoft Agent Framework brings those lines together.

Semantic Kernel
  enterprise app foundations
        +
AutoGen
  multi-agent orchestration
        =
Microsoft Agent Framework
  unified agent SDK and runtime

Microsoft’s launch materials describe Agent Framework as unifying Semantic Kernel’s enterprise foundations with AutoGen’s orchestration work. That means the question is no longer “enterprise stability or multi-agent flexibility?” The newer framework is meant to provide both.

The Practical Difference

The biggest practical difference is the programming model.

Semantic Kernel applications often revolve around the Kernel as the central object. Agents, plugins, services, and functions are registered around that kernel abstraction.

Microsoft Agent Framework shifts the center toward agents, tools, sessions, middleware, and workflows. Instead of everything depending on a kernel, agents can be created more directly from model clients and connected into workflows.

Microsoft’s Semantic Kernel migration guide highlights changes such as:

• Simplified agent creation
• Consolidated agent types
• Updated namespaces and packages
• More consistent provider interfaces
• Native sessions or threads for agent state
• Inline tool registration
• Better fit with Microsoft.Extensions.AI

This is not just a rename. It is a shift from “AI capabilities attached to a kernel” toward “agents and workflows as first-class application building blocks.”

Comparison Table

Question	Semantic Kernel	Microsoft Agent Framework
Best for new agent apps?	Usually no	Yes
Best for existing SK apps?	Yes, until migration is justified	Migration target
Core abstraction	Kernel, plugins, functions	Agents, tools, sessions, workflows
Multi-agent orchestration	Available, historically less central	First-class
Long-running workflows	Possible through SK patterns	First-class with checkpointing and hydration
Microsoft investment direction	Still relevant, but no longer the main agent path	Primary agent framework
Production status	Mature for many SK scenarios	1.0 for .NET and Python as of April 2026
Java support	Yes	.NET and Python are the main 1.0 SDKs; Go is preview

The table is intentionally practical rather than theological. The right question is not “which framework is better in the abstract?” It is “which framework matches the thing you are building now?”

Use Microsoft Agent Framework When

Choose Microsoft Agent Framework when you are building a new agentic application.

Good fits include:

• A new AI assistant
• A tool-using agent
• Multi-agent collaboration
• Long-running autonomous tasks
• Human-in-the-loop workflows
• Agent memory and session state
• MCP-based tool integration
• Graph-based workflow orchestration
• Azure AI Foundry-centered agent applications
• Applications that need future Microsoft agent features

Microsoft Agent Framework is especially attractive when the application is not just “call a model and display the answer.” If the system must plan, call tools, remember context, coordinate steps, pause for approval, resume later, or route through multiple agents, the newer framework is built for that.

This overlaps with broader workflow design ideas covered in the orchestrator pattern. Agent Framework gives you agent-oriented ways to coordinate multi-step work, while traditional orchestrators coordinate services and tasks more generally.

Use Semantic Kernel When

Semantic Kernel still makes sense in several situations.

Use it when:

• You already have a working Semantic Kernel application
• You depend on SK-specific plugins or connectors
• Your team is already productive with SK
• Your app is stable and migration risk is not worth it yet
• You need Java support today
• You are building non-agent AI features around existing SK patterns
• You have production support, governance, or internal standards built around SK

Existing applications should not migrate just because a newer framework exists. Migration has cost. It should be tied to a reason: new agent features, simpler architecture, long-running workflows, multi-agent orchestration, provider consistency, or future roadmap alignment.

The sensible path for a production SK app is usually:

Keep current app stable
        |
        v
Identify agent-heavy areas
        |
        v
Pilot Agent Framework in one slice
        |
        v
Migrate deliberately when value is clear

That is normal evolutive maintenance, not a panic rewrite.

Do You Need Either Framework?

Sometimes the answer is neither.

If your use case is:

• Summarize text
• Classify support tickets
• Extract fields from a document
• Generate a short draft
• Rewrite content in a tone
• Perform one model call with no tools

then an agent framework may be unnecessary. A direct call through an SDK or model client can be easier to understand, test, deploy, and secure.

Microsoft’s Agent Framework documentation makes a similar point: if you can write a function to handle the task, do that instead of using an AI agent.

Agents add value when the task needs autonomy, tool use, memory, planning, multi-step execution, or flexible decision-making. They add overhead when the task is simple and deterministic.

Agents vs Workflows

One useful part of Microsoft Agent Framework is that it separates agents from workflows.

Use an agent when the task is open-ended or conversational:

User asks a broad question
Agent decides which tools to call
Agent gathers information
Agent returns an answer

Use a workflow when the process has known steps:

Receive ticket
Classify severity
Check customer plan
Draft response
Request human approval
Send reply

That distinction is important. Developers often reach for autonomous agents when what they really need is an explicit workflow. If the steps are known, a workflow is usually easier to test and govern.

This is the same lesson behind what is a state machine?: when the lifecycle has clear states and transitions, explicit structure beats vague autonomy.

Why the Answer Changed

In 2024 and early 2025, a conservative team could reasonably choose Semantic Kernel for production and AutoGen for experimentation.

By 2026, Microsoft Agent Framework has become the convergence point.

The important milestones are:

• Semantic Kernel matured as an enterprise AI SDK
• Semantic Kernel agents reached general availability in 2025
• Microsoft introduced Agent Framework to unify SK and AutoGen concepts
• Agent Framework reached 1.0 for .NET and Python in April 2026
• Microsoft Learn now calls Agent Framework the next generation of both Semantic Kernel and AutoGen

So the decision changed because the product line changed. Semantic Kernel did not suddenly become useless. It became the predecessor for the agent-focused path.

What About AutoGen?

AutoGen matters because it influenced the multi-agent side of Microsoft Agent Framework.

If you are comparing only Semantic Kernel and Agent Framework, the practical takeaway is simple: many AutoGen-style ideas now live in the newer Agent Framework direction. Sequential orchestration, concurrent orchestration, handoff, group chat, and Magentic-style patterns are part of the Agent Framework story.

For new Microsoft-stack multi-agent work, the question is usually not “Semantic Kernel or AutoGen?” anymore. It is “Microsoft Agent Framework, or something outside Microsoft’s stack?”

Migration Strategy

If you have an existing Semantic Kernel codebase, avoid a big-bang migration unless there is a strong reason.

A safer migration strategy:

1. Inventory current SK usage
2. Separate simple model calls from agent workflows
3. Identify plugins, tools, memory, and connectors
4. Pick one agent-heavy feature as a pilot
5. Rebuild that slice in Microsoft Agent Framework
6. Compare code complexity, observability, and testability
7. Move additional workflows only when the value is clear

Migration is also a good moment to improve tests. Agent systems are harder to validate than ordinary deterministic code, so you need a mix of scenario tests, tool-call tests, evaluation datasets, and human review for high-risk behavior.

If your agent calls APIs, also consider contract testing vs integration testing, because tool and service contracts are still contracts even when an LLM chooses when to call them.

Production Considerations

Choosing the framework is only one part of building a production agent system.

You still need answers for:

• How are prompts versioned?
• Which tools can the agent call?
• What approvals are required?
• How is memory stored and deleted?
• How are tool calls logged?
• How are failures retried?
• How are hallucinations evaluated?
• How are sensitive actions gated?
• How are costs monitored?
• How are regressions caught?

Agent Framework gives you more built-in structure for many of these concerns, especially around sessions, middleware, workflows, checkpointing, tool integration, and evaluation. But a framework does not automatically make an agent safe or useful.

The architecture still matters. The guardrails still matter. The tests still matter.

This is also where AI-DLC vs traditional SDLC becomes relevant. AI changes the development workflow, but humans still own design, risk, validation, and release decisions.

Common Mistakes

Starting with a framework before defining the task. First decide whether you need a direct model call, an agent, or a workflow.

Using Semantic Kernel for new agent work out of habit. If the project is new and agent-focused, Microsoft Agent Framework is usually the better default now.

Rewriting stable Semantic Kernel apps without a reason. Migration should be justified by product value, maintainability, or roadmap alignment.

Making everything an autonomous agent. If the steps are known, use a workflow or ordinary code.

Ignoring evaluation and observability. Agent behavior must be measured, logged, reviewed, and improved over time.

Assuming a Microsoft framework means Azure-only. Agent Framework supports multiple model providers and tool standards, though it naturally integrates deeply with Microsoft Foundry and Azure.

Related Reading

If you are thinking through agent architecture, try these topics:

• AI-DLC vs traditional SDLC: how AI changes software delivery without removing human judgment
• AI-driven development lifecycle: where AI tools fit across planning, coding, testing, and maintenance
• The orchestrator pattern: how workflow coordination works outside the agent-specific world
• What is a state machine?: when explicit states beat open-ended decision-making
• Contract testing vs integration testing: useful when agents call tools and APIs
• OpenAI Codex review: another angle on autonomous coding agents and developer workflows

For external references, start with Microsoft’s Agent Framework overview, the Semantic Kernel documentation, the Semantic Kernel to Agent Framework migration guide, and the Agent Framework 1.0 announcement.

Frequently Asked Questions

Is Microsoft Agent Framework replacing Semantic Kernel? For agent development, Microsoft positions Agent Framework as the successor to Semantic Kernel and AutoGen. Semantic Kernel still exists and remains relevant for existing applications and some non-agent AI integration scenarios.

Should I start new agent projects with Semantic Kernel? Usually no. For a new agent-focused project in 2026, Microsoft Agent Framework is the better default because it is the current agent platform and the center of new Microsoft investment.

Should I migrate my existing Semantic Kernel app immediately? Not automatically. If the app is stable and working, migrate deliberately. Start with features that would benefit from Agent Framework’s newer agent, workflow, session, or orchestration capabilities.

Is Microsoft Agent Framework production-ready? Microsoft announced Agent Framework version 1.0 for .NET and Python in April 2026, describing it as production-ready with stable APIs and long-term support.

Do I need an agent framework for a simple AI feature? Not always. If the feature is a single prompt or deterministic transformation, a direct model call may be simpler. Use an agent framework when you need tools, memory, planning, autonomy, workflows, or multi-step coordination.

Conclusion

For new Microsoft-stack agent applications, use Microsoft Agent Framework. It is the current successor to Semantic Kernel and AutoGen, reached 1.0 for .NET and Python in 2026, and is where Microsoft’s agent platform work is concentrated.

Semantic Kernel still matters. It has a real installed base, mature patterns, and existing production applications. But its role has shifted: keep it where it is already working, and treat Microsoft Agent Framework as the default for new agent and orchestration work.

The deeper decision is not just Semantic Kernel versus Agent Framework. It is whether you need an agent at all. Use ordinary code for deterministic tasks, direct model calls for simple AI features, workflows for known processes, and agents when autonomy, tools, memory, and flexible reasoning actually earn their keep.

Written by the Workshelve team, who write practical explainers on data integrity, networking, and developer tooling.