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

Key takeaways

• Microsoft describes Agent Framework as the successor to Semantic Kernel and AutoGen.
• Semantic Kernel integrates models and application functions; Agent Framework brings agents, sessions, tools, and explicit workflows together.
• Existing Semantic Kernel applications need a concrete migration reason and a plan for preserving behavior.
• Neither framework removes the need for authorization, controlled side effects, and evidence that the application works.

Semantic Kernel and Microsoft Agent Framework are related Microsoft projects for building AI applications. Semantic Kernel connects models with application functions and services. Agent Framework provides a newer foundation for agents and workflows, and Microsoft describes it as the successor to both Semantic Kernel and AutoGen in its framework overview.

For a new agent application, Agent Framework is a sensible starting point to evaluate. For an existing Semantic Kernel application, the decision depends on the capabilities you need, the integrations you rely on, and the cost of preserving behavior during migration.

A successor relationship tells you the direction of a platform. It does not establish that replacing a working application immediately is your highest-value engineering task.

What Semantic Kernel Does

Semantic Kernel is an open-source SDK for integrating AI models into C#, Python, and Java applications. Its plugins expose application functions so that a model can request useful actions, with the application executing those functions and returning results.

For example, a support assistant might have a function that retrieves an order and another that searches product documentation. The framework helps connect those functions to the model-driven conversation.

Semantic Kernel also has agent capabilities. Describing it as only a prompt wrapper would obscure why existing teams have built substantial applications around it.

What Microsoft Agent Framework Adds to the Decision

Agent Framework organizes applications around agents, tools, sessions, and workflows. Sessions support conversation state, while workflows provide explicit execution paths connecting agents and functions.

Its current documentation covers .NET and Python and also lists Go in public preview. Language support and feature maturity should therefore be checked for the particular packages you intend to use, rather than treated as identical across implementations. See the current Agent Framework overview for those distinctions.

For application design, the useful question is how much of the process should be chosen dynamically and how much should follow a defined sequence.

Compare Them Through a Support Workflow

Imagine an assistant helping a customer with a damaged order. It needs to find the order, read the returns policy, gather missing information, and prepare a proposed resolution.

The conversational part benefits from flexible tool selection. A customer may provide an order number immediately or need help finding it. The assistant can decide which information to request next.

Issuing a replacement has a different shape. The application must establish eligibility, verify authorization, obtain any required approval, and submit the operation without creating duplicate orders.

That sequence is a reason to evaluate an explicit workflow. It is also a reason to keep business rules in application code. A model’s statement that a replacement is approved should not itself authorize the transaction.

This distinction connects to the broader orchestrator pattern: coordination becomes easier to reason about when consequential transitions have an identifiable owner.

Semantic Kernel vs Agent Framework: Practical Choices

Your situation	A useful next step
Building a new agent application	Evaluate Agent Framework against the providers and language you need
Maintaining a reliable Semantic Kernel application	Identify a missing capability or maintenance benefit before migrating
Adding a process with mandatory steps	Prototype an explicit workflow and its failure paths
Calling a model once to summarize text	Assess whether a direct model client is sufficient
Depending on specialized plugins or stored conversations	Inventory those dependencies before choosing a migration boundary

These are application-design recommendations, not a claim that one framework automatically makes a system reliable.

For the support example, evaluate the complete operation. Can the customer leave and return later? What happens if replacement creation succeeds but the response is lost? Where is approval recorded? A successful chat demonstration does not answer those questions.

What Changes When You Migrate?

Microsoft’s Semantic Kernel migration guide describes changes to agent construction, tool registration, invocation, and conversation state. In .NET, Agent Framework uses the Microsoft.Agents.AI namespace and shared message types from Microsoft.Extensions.AI. Tool registration can use functions directly rather than requiring the same kernel-and-plugin setup.

The guide also documents Python compatibility for converting existing KernelFunction instances into Agent Framework tools. That can help preserve useful functions during a staged transition, but it does not convert an entire application’s behavior automatically.

Treat stored state as its own migration problem. Existing conversations may contain provider-specific identifiers, serialized messages, or references to tools whose names have changed. Decide whether those conversations continue through the old path, are explicitly transformed, or start fresh.

Migrate One Capability Before the Whole Application

A reasonable first candidate in the support example is read-only order lookup. Keep its inputs, authorization rules, and result shape stable while changing the agent integration around it.

Compare the resulting behavior using representative conversations. Include an unknown order, missing information, an unavailable dependency, and a request for another customer’s data. Inspect which tools were invoked and what arguments they received, not just the final prose.

Only expand the migration when you can explain the differences. Replacement creation should come later because retries and duplicate execution have more significant consequences than a failed lookup.

Add operational visibility along the way. Distributed tracing can connect an agent interaction with downstream service calls, while carefully chosen logs can record decisions without retaining unnecessary customer content.

Framework Choice Does Not Replace Application Design

Neither framework can determine your business permissions or choose acceptable recovery behavior for you. Session support also does not, by itself, establish that every operation survives a process restart correctly.

Choose where state is stored, how approvals are enforced, and how repeated requests are handled. Then verify those choices in the environment where the application will run.

For new work, begin by evaluating Agent Framework’s fit. For existing work, make the migration case specific: a needed workflow capability, a simpler integration, or a maintenance benefit you can demonstrate. That gives the team a reason to change and a concrete way to judge whether the change helped.

Choose the framework around the behavior you need to control, and judge a migration by the behavior you preserve.