Microsoft Agent Framework has become the company’s clearest long-term path for teams building agentic applications on the Microsoft stack. Microsoft’s own documentation now describes it as the next generation of both Semantic Kernel and AutoGen, and the company has published migration guidance for both codebases. For engineering leaders, that changes the question from Should we pay attention? to How do we migrate without breaking working systems?
If your team already has prototypes, internal copilots, or multi-agent workflows built on Semantic Kernel or AutoGen, the good news is that this is not a ground-up rewrite. The core ideas still look familiar: model-backed agents, tools, sessions, orchestration, and enterprise controls. But the abstractions have changed enough that a direct copy-paste migration is rarely the right move. The better path is to understand what Microsoft is standardizing, what it is simplifying, and where the new framework changes how production agent systems should be designed.
Why teams are moving now
Microsoft is treating Agent Framework as the successor layer rather than as just another SDK in the portfolio. That matters because it signals where future samples, integrations, and enterprise patterns are likely to concentrate. Agent Framework brings together ideas that previously lived across Semantic Kernel and AutoGen into one model that works across .NET and Python, with support for workflows, tools, telemetry, and interoperability patterns such as MCP, A2A, and AG-UI.
For teams that started on Semantic Kernel, the migration story is about simplification. Agent creation is more direct, tools are registered more cleanly, and the framework leans on shared AI abstractions instead of older Kernel-heavy patterns. For teams coming from AutoGen, the bigger shift is orchestration style. Microsoft is moving from event-driven multi-agent patterns toward a more typed, graph-based workflow model that is easier to reason about, govern, and resume in production.
There is also a practical timing reason to act now: Microsoft has already published official migration guides and positioned the framework as the path forward for new development. Even if you do not migrate every workload immediately, you should stop treating older abstractions as the default foundation for net-new agent projects.
What actually changes from Semantic Kernel
The most visible change for Semantic Kernel teams is that Microsoft Agent Framework removes a lot of the ceremony around the Kernel object. In older Semantic Kernel agent patterns, the Kernel often sat at the center of model configuration, plugin registration, and invocation. In Agent Framework, agent creation is more direct and more uniform across providers.
That sounds small, but it has real consequences for migration planning:
- Namespaces and core types change. Teams need to update imports and adjust to the newer Microsoft AI abstractions.
- Agent creation gets simpler. Instead of building around a Kernel and then attaching the agent to it, you can create agents directly from the underlying chat client or provider extensions.
- Tool registration is cleaner. Semantic Kernel’s plugin wrapping and function decoration model gives way to a more direct tool definition style.
- Invocation changes from
Invokepatterns toRunpatterns. That usually means small code edits but broader testing around streaming, sessions, and response handling. - Response handling is more structured. Instead of treating everything as chat-like output, Agent Framework returns richer response objects that can include tool calls, reasoning updates, and multiple messages.
In practice, this means the migration is not just a package update. It is a cleanup opportunity. If your Semantic Kernel codebase accumulated wrappers, helper classes, or brittle plugin conventions over time, now is the right moment to flatten that architecture instead of recreating the same complexity in a new framework.
What actually changes from AutoGen
For AutoGen teams, the migration is more conceptual. AutoGen popularized several multi-agent patterns that made experimentation fast, especially around group chat and event-driven coordination. Agent Framework keeps the multi-agent ambition, but Microsoft is steering teams toward typed workflows with clearer execution paths.
That shift matters because many real production failures happen in the gaps between agents, not inside the model call itself. Enterprise teams need to know which step ran, what input moved across the edge, why a handoff happened, and how to pause or resume work when a human needs to approve the next action. Agent Framework is built more explicitly for that style of system.
Key migration differences for AutoGen users include:
- Workflow-first orchestration. Instead of leaning primarily on event-driven team behavior, you design more explicit data flow between executors.
- Different default agent behavior. Agent Framework agents are multi-turn by default and continue invoking tools until they can return a final answer.
- Hosted tools and newer provider patterns. The framework includes more direct support for capabilities such as web search and code-interpreter-style hosted tools, depending on provider support.
- Human-in-the-loop and checkpointing concepts are more central. This is important for approval-heavy business workflows.
- Middleware and observability are stronger first-class concerns. That makes it easier to build production guardrails instead of bolting them on afterward.
The biggest mistake AutoGen teams can make is trying to recreate every existing team pattern one-for-one. Some designs should be translated into explicit workflows instead. If your current system depends on implicit back-and-forth between agents, this migration is the right time to decide whether that interaction is actually necessary or whether a simpler graph will be easier to operate.
A practical migration plan for production teams
The safest way to move is not a big-bang rewrite. It is a staged migration with a clear inventory and a few opinionated decisions up front.
1. Classify workloads before you touch code
Start by separating your existing agent systems into three buckets:
- Single-agent utilities such as summarizers, support helpers, or retrieval workflows.
- Multi-step workflows that involve tools, approvals, or business process stages.
- True multi-agent systems where specialized agents coordinate on a larger task.
The first bucket is usually the easiest to migrate. The third bucket often needs the most redesign.
2. Freeze net-new development on legacy patterns
If teams are still creating fresh Semantic Kernel or AutoGen abstractions for new projects, you will extend your migration debt. Keep older systems running, but move new work onto Agent Framework unless there is a strong blocker.
3. Migrate the smallest high-value workflow first
Do not begin with your most complex orchestration graph. Pick a workflow that matters to the business but is small enough to port in days, not months. This gives your team a real migration pattern, code examples, and deployment lessons before higher-risk systems move.
4. Rebuild observability while you migrate
Migration is the right moment to standardize traces, logging, prompt versioning, and tool telemetry. If you simply port logic without improving observability, you will miss one of the framework’s biggest advantages.
5. Test behavior, not just syntax
An agent that compiles is not necessarily an agent that behaves the same way. Validate routing, tool usage, approvals, session behavior, retries, and failure recovery. Teams often underestimate how much model behavior changes when tool schemas, instructions, or message handling are refactored.
Common migration pitfalls to avoid
Pitfall one: treating the migration as a rename exercise. Agent Framework is close enough to feel familiar, but different enough that a mechanical port can preserve bad architecture.
Pitfall two: overusing autonomous multi-agent patterns. In many business workflows, an explicit workflow with strong checkpoints is better than a freeform conversation among agents.
Pitfall three: ignoring provider fit. Agent Framework supports multiple providers, but not every model or tool combination behaves the same way. Validate your exact provider setup early.
Pitfall four: forgetting governance. The more powerful your tools become, the more you need approval policies, access boundaries, and clear audit trails.
Pitfall five: migrating code without migrating operating practice. Production agents need versioning, evals, trace review, and rollback plans. The framework change should strengthen all of those.
What this means for enterprise AI teams
Microsoft Agent Framework is not just a new SDK. It is Microsoft’s attempt to consolidate its agent story into one more production-oriented path. That is good news for teams that were previously forced to choose between experimentation speed and enterprise structure.
The deeper takeaway is that the market is moving away from loosely connected agent demos and toward better-defined operating layers: typed workflows, stronger tooling, provider flexibility, checkpointing, and governance. Migration work can feel expensive in the short term, but it often pays for itself by reducing framework sprawl and making future agent systems easier to maintain.
If your organization already has useful work running on Semantic Kernel or AutoGen, the right move is not to panic and rewrite everything. It is to set a transition plan now, migrate new development first, and use the shift to simplify the systems you actually want to live with in production next year.