Microsoft Agent Framework vs AutoGen: Your Migration Guide (2026)

Microsoft Agent Framework vs AutoGen: Your Migration Guide (2026)

If you built multi-agent systems on AutoGen over the last three years, the clock is now visibly ticking. Microsoft has placed AutoGen in maintenance mode - community-managed, limited to bug fixes and security patches - and shipped its successor: Microsoft Agent Framework 1.0, which reached general availability in April 2026.

This is not a rebrand. Microsoft Agent Framework (MAF) merges AutoGen and Semantic Kernel into one SDK with a genuinely different architecture, and the migration is real engineering work. Here is what happened, how the pieces map, a migration path you can actually follow, and - just as important - when you should not migrate at all.

What happened to AutoGen

AutoGen came out of Microsoft Research in 2023 and pioneered the conversation-driven multi-agent pattern: agents as ConversableAgent instances exchanging messages, group chats with a manager routing turns. It was hugely influential - we covered it in our framework comparison - but it was always a research framework being pressed into production duty, and the ecosystem fractured along the way (some of the original authors forked it as AG2 in late 2024, while Microsoft continued its own line).

Microsoft resolved the ambiguity by converging its two agent efforts. AutoGen (the research innovation line) and Semantic Kernel (the enterprise .NET line) were unified into Microsoft Agent Framework, built by the same teams. The official position is blunt: AutoGen gets security patches and bug fixes only; new users should start with MAF; existing users get a migration guide.

The practical consequences of maintenance mode:

• No new model or API support. As providers ship new capabilities (structured outputs changes, new tool types, protocol updates), AutoGen will not track them.
• A shrinking talent and content pool. Tutorials, Stack Overflow answers, and hiring pipelines follow the active framework.
• Compliance risk accrues quietly. A frozen dependency at the heart of an autonomous system is exactly what an audit under the UAE’s agentic AI push will flag.

What Microsoft Agent Framework actually is

MAF is a single SDK for .NET and Python with two layers:

Agents - the simple layer. An agent wraps a model, instructions, and tools, and handles multi-turn conversations with thread-based state. This is the part that feels familiar coming from AutoGen’s AssistantAgent.

Workflows - the orchestration layer, and the biggest conceptual change. Instead of AutoGen’s event-driven conversations, MAF models multi-agent systems as typed, graph-based Workflows: executors (agents or functions) connected by edges that route data, activating when their inputs are ready. The GA release ships stable orchestration patterns - sequential, concurrent, handoff, group chat, and Magentic-style planning - all with streaming, checkpointing, pause/resume, and human-in-the-loop approvals built in.

On top of that, MAF adds what enterprise teams kept bolting onto AutoGen by hand:

• Declarative agents - define instructions, tools, memory, and orchestration topology in version-controlled YAML and load them with one call.
• Observability - OpenTelemetry-based tracing and telemetry inherited from the Semantic Kernel lineage.
• Protocol support - native MCP for tool discovery and A2A for cross-runtime agent collaboration (see our MCP vs A2A explainer for why both matter).
• Azure integration - first-class paths into Azure AI Foundry for hosted deployment.

Feature mapping: old to new

The pattern in that table: the leaf nodes port easily, the orchestration core does not. AutoGen let agents converse and emergent behavior route the work; MAF wants you to make the routing explicit as a typed graph. That is a better fit for production - deterministic, checkpointable, auditable - but it means your GroupChat logic gets redesigned, not transliterated.

A migration path that works

We run these migrations as four phases. Steal the structure even if you do it in-house.

Phase 1: Assessment (week 1). Inventory every agent, tool, and conversation pattern in the AutoGen estate. Classify each system: single-agent (easy), sequential team (moderate), dynamic group chat (redesign). Capture current behavior as an eval set - real inputs, expected outputs, tool-call traces. Without this baseline you cannot prove the migration did not regress anything.

Phase 2: Mapping (week 1-2). For each system, draw the target Workflow graph explicitly: which executors, which edges, where state checkpoints, where humans approve. This is where you discover that your SelectorGroupChat was really a router plus three specialists - which becomes a clean handoff Workflow - or that it was genuinely emergent, which forces a design conversation AutoGen let you avoid.

Phase 3: Rewrite (weeks 2-6, per system). Port tools first (mostly mechanical - decorator plus schema inference), then single agents, then workflows. Run old and new side by side against the Phase 1 eval set. Resist the urge to “improve” behavior mid-migration; land parity first, then iterate.

Phase 4: Validation (week 6-8). Full eval pass, load testing on the checkpoint/resume paths, observability verification (can you trace a failed run end to end?), and a staged cutover with the AutoGen system kept warm as rollback for one release cycle.

Small estates (one or two agents, simple tools) compress to a couple of weeks. Large dynamic group-chat systems are honest multi-month projects - budget accordingly.

When NOT to migrate

Migration is not automatically the right call. Skip it, or defer it, when:

• The system is being retired. A stable AutoGen pipeline with 12 months of life left does not justify a rewrite. Security patches still flow; let it age out.
• You are on AG2 territory anyway. If what you liked about AutoGen was the conversational research-style programming model, AG2 continues that line under active community development. It is a legitimate continuity option for research and internal tooling - less so for regulated production.
• Your Microsoft commitment is soft. MAF is excellent inside the Microsoft ecosystem - .NET shops, Azure AI Foundry deployments, Entra ID and Microsoft 365 integration. If none of that describes you, a forced migration is the ideal moment to reconsider the ecosystem choice altogether, because you are paying the rewrite cost either way.

Alternatives if you are leaving the Microsoft ecosystem

Since the orchestration layer must be rewritten regardless, the migration is a rare free look at the rest of the market:

LangGraph - the production default for explicit workflow control. Agents as directed graphs with typed state, durable checkpointing to Postgres, human-in-the-loop interrupts, and LangSmith observability. Conceptually the closest non-Microsoft analog to MAF Workflows - if you like where MAF is going but not the vendor, this is the comparison to run. Python and JS.

Claude Agent SDK - the strongest option if your agents do environment-heavy autonomous work (code, files, documents, ops). Built-in tools, lifecycle hooks for policy enforcement, subagents for parallel delegation, and native MCP. See our full Claude Agent SDK vs OpenAI Agents SDK breakdown for how it compares to the other provider-native SDK.

OpenAI Agents SDK - lightweight handoff-based orchestration, strong tracing defaults, and voice/realtime support; the natural fit for conversational multi-agent systems.

All three, like MAF, speak MCP - so tools you build as MCP servers survive whichever way you jump. That is the most durable investment in this whole migration, and it is worth doing properly (our skills and plugins practice builds exactly these).

AutoGen vs Microsoft Agent Framework: summary table

The bottom line

AutoGen earned its place in agent history, but building on it in 2026 means building on a frozen foundation. If you are staying in the Microsoft ecosystem, Microsoft Agent Framework is a genuine upgrade - the enterprise features it ships (checkpointing, approvals, telemetry, MCP/A2A) are the things production teams were hand-rolling on top of AutoGen anyway. Plan the migration as an orchestration redesign with tool reuse, baseline everything with evals, and use the moment to decide deliberately whether Microsoft’s stack is still the right home. New to agents entirely? Start with our AI agents hub for the foundations.

NomadX is an AI agents consultancy in Dubai that runs framework migrations and production agent builds for UAE and GCC enterprises - including AutoGen-to-MAF assessments with a fixed-scope roadmap. Book a free 30-minute migration consultation and bring your agent inventory.