Quick Verdict: Should Developers Use Microsoft Agent Framework 1.0?

Microsoft Agent Framework 1.0 is a production-ready agent framework for .NET and Python teams that need stable APIs, long-term support, graph workflows, tool calling, middleware, memory, and multi-agent orchestration in one Microsoft-backed stack. Microsoft says the 1.0 GA milestone landed on April 2, 2026, and repository metadata checked on June 15, 2026 showed 11,343 GitHub stars, 1,906 forks, and 675 open issues for microsoft/agent-framework. My practical verdict is simple: use it when agent behavior must be durable, observable, and governed; avoid it when a deterministic function, queue worker, or simple LLM call solves the job. The framework is strongest for enterprise teams that need human approval, checkpointing, state, telemetry, and Azure AI Foundry alignment. The clear takeaway: Microsoft Agent Framework 1.0 is a serious runtime, not a lightweight prompt wrapper.

For new Microsoft-stack agent projects, I would start on Microsoft Agent Framework unless an existing Semantic Kernel application is stable and already meets requirements. For quick experiments, LangGraph, the OpenAI Agents SDK, or even a plain SDK call may still be faster. The key is to price in operational work from day one.

What Is Microsoft Agent Framework 1.0?

Microsoft Agent Framework 1.0 is the unified successor direction for Microsoft agent development, combining AutoGen-style agent abstractions with Semantic Kernel production features such as type safety, middleware, telemetry, filters, session state, and broad model connectivity. Microsoft Learn describes the framework as a way to build agents and graph-based workflows, while the launch messaging emphasizes stable .NET and Python APIs with long-term support. In practice, that means the framework gives developers a common place to define agents, attach tools, manage conversation and task state, orchestrate multi-agent flows, and integrate with enterprise observability. It also keeps an important boundary: Microsoft explicitly advises using ordinary functions when a task is deterministic. The useful definition is not “LLM app framework”; it is “agent runtime for controlled autonomy.” The clear takeaway: use Microsoft Agent Framework when autonomy and state justify framework complexity.

That boundary matters. Many production “agent” failures are not model failures; they are ownership failures around retries, data access, tool permissions, audit logs, and escalation. MAF does not remove those responsibilities, but it gives them first-class places to live.

What Changed from Semantic Kernel and AutoGen?

Microsoft Agent Framework changes the old Semantic Kernel and AutoGen story by reducing two overlapping agent paths into one supported framework while preserving the strongest ideas from both projects. On June 15, 2026, microsoft/autogen had 58,956 GitHub stars and its README directed new users toward Microsoft Agent Framework, while microsoft/semantic-kernel still had 28,120 stars and active development. That status tells you the migration story: AutoGen is no longer the default starting point for new Microsoft agent work, and Semantic Kernel remains active but is no longer the only production agent surface. AutoGen contributed approachable multi-agent experimentation; Semantic Kernel contributed enterprise integration, type discipline, and connectors. MAF tries to make those pieces feel like one stack instead of two roadmaps. The clear takeaway: new greenfield agent work should evaluate MAF first.

Which 1.0 Features Are Stable?

The stable Microsoft Agent Framework 1.0 surface includes single-agent abstractions, service connectors, tool integration, middleware hooks, agent memory and context providers, graph workflows, and common multi-agent orchestration patterns. Independent coverage of the 1.0 release emphasized that Microsoft positioned these APIs as production-ready for .NET and Python, while preview areas were called out separately. That distinction is important for architects because “agent framework” announcements often mix reliable core APIs with demo-friendly previews. In this release, the dependable value is the boring work: agents can call tools, preserve context, run through explicit workflows, and expose hooks where production teams can add policy, logging, validation, and recovery. Graph workflows are especially useful because they make execution paths inspectable instead of hiding everything inside a single model loop. The clear takeaway: build first on stable agent and workflow primitives.

The feature set is broad enough to cover real applications: support bots that open tickets, internal copilots that query private systems, and task agents that coordinate approval flows. The biggest design choice is whether to model a process as an agent, a workflow, or a normal function.

How Does Multi-Agent Orchestration Work?

Multi-agent orchestration in Microsoft Agent Framework works by giving developers explicit patterns for coordinating agents, including sequential execution, concurrent execution, handoff, group chat, and Magentic-One-style coordination. Microsoft lists those patterns as part of the stable orchestration story, and they matter because agent systems fail differently when work is serialized, parallelized, or delegated. A sequential pattern fits review pipelines where step two depends on step one; concurrent execution fits independent research or validation tasks; handoff fits routing from triage to specialist agents; group chat fits collaborative reasoning; Magentic-One fits more autonomous multi-step problem solving. The advantage over ad hoc loops is that orchestration becomes a named architecture choice rather than hidden prompt behavior. That improves testing, observability, and incident review. The clear takeaway: choose the weakest orchestration pattern that satisfies the workflow.

What Did Build 2026 Add?

Build 2026 expanded the Microsoft Agent Framework story from framework unification toward operational agent runtime capabilities, including Agent Harness, Foundry Hosted Agents, deeper Copilot SDK integration, and CodeAct optimization. Microsoft’s Build post said Agent Harness covers production plumbing such as shell and file access, human approvals, context management, memory, todo tracking, plan and execute modes, skills, background agents, web search, approvals, and OpenTelemetry. The same update positioned Foundry Hosted Agents as a deployment path with managed identity, scaling, persistent session state, isolation, observability, and versioning. CodeAct is the most concrete performance example: a representative workload reportedly improved from 27.81 seconds and 6,890 tokens to 13.23 seconds and 2,489 tokens. The clear takeaway: the platform ambition is bigger than the 1.0 core.

I would treat the Build 2026 additions as a second adoption phase. Start with stable agents and workflows, then selectively evaluate hosted runtime, CodeAct, and UI adapters when they solve a measured problem. That keeps adoption practical instead of chasing every preview-adjacent capability.

How Is the Python and .NET Developer Experience?

The Python and .NET developer experience in Microsoft Agent Framework is credible because both runtimes are first-class release targets, not one primary SDK with a thin secondary port. PyPI metadata checked on June 15, 2026 showed the agent-framework Python package at version 1.8.1 with a Python requirement of 3.10 or newer, while NuGet metadata for Microsoft.Agents.AI included recent versions through 1.10.0. That cadence matters for enterprise teams that run mixed stacks: Python teams can build data-heavy agents, while .NET teams can integrate with existing services, identity, dependency injection, and Microsoft.Extensions patterns. The best developer experience is likely in organizations that already use Azure SDKs, OpenTelemetry, and typed service boundaries. The clear takeaway: MAF is a real dual-runtime framework, especially attractive to .NET shops.

The tradeoff is that two first-class runtimes still require discipline. Shared patterns, evaluation data, tool contracts, and deployment policies should be documented outside either language. Otherwise, Python and .NET agents drift into subtly different behavior.

Is Microsoft Agent Framework Production Ready?

Microsoft Agent Framework 1.0 is production ready for its core APIs, but production readiness still depends on how the application handles state, checkpointing, observability, governance, human approval, model risk, tool risk, and cost. Microsoft Learn explicitly warns that third-party systems, non-Azure models, data sharing, costs, reliability, security, compliance, and responsible AI mitigations remain the builder’s responsibility. That is the right warning. A framework can provide middleware, telemetry, session state, and workflow structure, but it cannot decide which customer records an agent may access or when a human must approve a tool action. In my experience, the production question is less “can the framework call tools?” and more “can operators explain what happened after a bad tool call?” The clear takeaway: MAF supplies production hooks, not production judgment.

For serious deployments, I would require tool allowlists, input/output validation, audit logs, trace correlation, retry policies, budget controls, and human approval gates for destructive operations. The framework gives teams places to attach those controls; teams still need to design them.

How Should Semantic Kernel Users Migrate?

Semantic Kernel users should migrate to Microsoft Agent Framework when they need simpler agent abstractions, graph workflows, richer multi-agent coordination, or a forward-looking Microsoft agent surface for new development. Semantic Kernel remains active, with repository metadata checked on June 15, 2026 showing 28,120 stars, 4,649 forks, 282 open issues, and recent pushes, so this is not an emergency rewrite signal. The sensible path is incremental: keep stable SK applications running, identify where agent state or orchestration is becoming awkward, then introduce MAF around new autonomous workflows. Reuse existing plugins, service boundaries, telemetry conventions, and responsible AI controls wherever possible. The migration should be driven by clearer architecture, not novelty. The clear takeaway: migrate Semantic Kernel systems when MAF solves a concrete workflow problem.

Start with a thin pilot: one agent, one or two tools, clear acceptance tests, and trace coverage. If that pilot is easier to operate than the SK version, expand. If not, keep the existing implementation and revisit when requirements change.

How Should AutoGen Users Migrate?

AutoGen users should migrate to Microsoft Agent Framework for new production work because AutoGen is now positioned as maintenance-oriented while MAF is the supported Microsoft path for stable agent APIs and enterprise features. The key signal is not just branding: on June 15, 2026, the AutoGen repository still had 58,956 stars and 8,896 forks, but its README directed new users to Microsoft Agent Framework. That means existing AutoGen systems deserve maintenance and careful transition planning, while new systems should avoid deepening dependence on the older project. The migration opportunity is to keep useful multi-agent concepts while adding stronger state management, middleware, telemetry, workflow structure, and governance. The clear takeaway: AutoGen teams should move new builds to MAF and migrate old builds selectively.

Do not port every AutoGen conversation pattern mechanically. Some group-chat flows should become explicit workflows; some autonomous loops should become handoff patterns; some agents should disappear into deterministic functions. Migration is a chance to simplify.

How Does Microsoft Agent Framework Compare with LangGraph, OpenAI Agents SDK, CrewAI, and Google ADK?

Microsoft Agent Framework compares best as a Microsoft-native production agent runtime, while LangGraph is stronger for graph-centric Python agent architectures, the OpenAI Agents SDK is strongest for OpenAI-first applications, CrewAI is convenient for role-based team prototypes, and Google ADK fits teams aligned with Google’s AI platform. LangChain’s framework guide frames MAF primarily for Microsoft-stack teams, which matches my read. MAF’s advantage is enterprise integration: Azure AI Foundry alignment, .NET support, Microsoft.Extensions.AI, OpenTelemetry, middleware, and a migration path from Semantic Kernel and AutoGen. Its disadvantage is ecosystem gravity; if your stack is model-provider-neutral and Python-first, LangGraph may feel more natural. If you only need OpenAI tool calling and tracing, the OpenAI Agents SDK may be leaner. The clear takeaway: choose MAF for Microsoft production alignment, not generic framework fashion.

What Are the Main Risks and Limitations?

The main Microsoft Agent Framework risks are preview-feature temptation, Azure gravity, responsibility boundaries around third-party systems, and the operational burden that comes with real agent autonomy. The research brief separates stable 1.0 areas from newer or preview-adjacent pieces such as DevUI, Foundry Hosted Agent integration, Foundry observability and evaluations, AG-UI, CopilotKit, ChatKit adapters, skills, GitHub Copilot SDK, Claude Code SDK, and Agent Harness. That split should shape adoption plans. A team can use stable agents and workflows today while still testing hosted runtime, UI adapters, and harness features behind clear gates. The other limitation is cultural: developers may use agents where a deterministic workflow would be cheaper, safer, and easier to debug. The clear takeaway: MAF is powerful, but restraint is part of good architecture.

My rule is blunt: if you can express the behavior as a normal function with deterministic inputs, do that first. Reach for MAF when you need tool-using autonomy, ambiguous decision handling, durable context, or multi-agent coordination.

Who Should Adopt It Now?

Microsoft Agent Framework should be adopted now by teams building production agents in .NET or Python that need enterprise controls, durable workflows, multi-agent orchestration, OpenTelemetry visibility, human approval, and Microsoft ecosystem integration. The strongest adopters are likely internal platform teams, customer support automation teams, compliance-sensitive product teams, and organizations already using Azure AI Foundry or Semantic Kernel. Teams should wait if their use case is still a chatbot prototype, a single prompt chain, or a deterministic data pipeline. Teams should choose another framework when they are deeply invested in LangGraph patterns, OpenAI-only workflows, or Google Cloud-native agent tooling. The decision is not about whether MAF is “best”; it is about whether its operational model matches your constraints. The clear takeaway: adopt MAF when governance and Microsoft integration are core requirements.

Final Verdict: Is Microsoft Agent Framework 1.0 Good?

Microsoft Agent Framework 1.0 is good because it gives Microsoft developers a coherent production agent path after years of overlap between Semantic Kernel and AutoGen, and it does so with stable .NET and Python APIs, graph workflows, orchestration patterns, middleware, state, and observability hooks. The 2026 release is not magic, and it does not remove hard problems around tool safety, model evaluation, cost, compliance, and human oversight. But it finally gives architects a reasonable default answer for Microsoft-native agent systems: start with MAF, keep deterministic work deterministic, and graduate into hosted agents or harness features only when operational evidence supports it. Compared with the broader ecosystem, MAF is less interesting as a toy demo framework and more interesting as enterprise agent infrastructure. The clear takeaway: Microsoft Agent Framework 1.0 is a strong, pragmatic default for Microsoft-stack production agents.

The best sign is the documentation’s own restraint. Advising developers to use ordinary functions when possible is exactly what a production framework should say. Autonomy is expensive; MAF is useful when that expense buys flexibility, recovery, and coordination.

FAQ

Microsoft Agent Framework FAQs usually come down to migration timing, framework choice, production readiness, language support, and how much of the old Semantic Kernel or AutoGen knowledge still applies. The short version is that MAF is the forward-looking Microsoft agent framework for new production work, while existing Semantic Kernel and AutoGen systems should be migrated only when there is a clear architectural benefit. The June 15, 2026 package and repository signals show active development across MAF, Semantic Kernel, and related libraries, but the strategic direction is clear: Microsoft wants one agent framework for .NET and Python. Developers should treat the 1.0 core as usable and treat newer hosted, harness, and UI integrations with normal preview caution during rollout planning. The clear takeaway: choose based on production needs, not announcement momentum.

Is Microsoft Agent Framework replacing Semantic Kernel?

Microsoft Agent Framework is not a simple drop-in replacement for every Semantic Kernel application, but it is the better starting point for new agent-centric workflows. Semantic Kernel remains active and useful for existing LLM applications, plugins, connectors, and enterprise integration. MAF becomes compelling when the system needs explicit agents, graph workflows, multi-agent patterns, checkpointing, or long-running state. For stable SK apps, migrate by capability need, not fear.

Is AutoGen dead after Microsoft Agent Framework 1.0?

AutoGen is not dead, but it is no longer the default recommendation for new Microsoft agent projects. Its repository still has a large community footprint, but Microsoft now points new users toward Microsoft Agent Framework. Existing AutoGen systems should be maintained carefully, then migrated when production features such as observability, middleware, state, and governance become important. New production work should begin with MAF.

Can I use Microsoft Agent Framework without Azure?

Microsoft Agent Framework can be used as a framework for .NET and Python agent development, but its strongest production story is aligned with the Microsoft and Azure ecosystem. If your team wants Azure AI Foundry, managed identity, OpenTelemetry conventions, Microsoft.Extensions.AI, or Microsoft governance patterns, MAF fits naturally. If your deployment is intentionally cloud-neutral or non-Microsoft, compare LangGraph and the OpenAI Agents SDK before committing.

Is Microsoft Agent Framework better than LangGraph?

Microsoft Agent Framework is better than LangGraph for Microsoft-stack teams that need first-class .NET support, Azure alignment, enterprise middleware, and a migration path from Semantic Kernel or AutoGen. LangGraph may be better for Python-first teams that want very explicit graph control and already use LangChain ecosystem tooling. The better framework is the one whose operational assumptions match your deployment, tracing, model, and governance requirements.

Should small teams use Microsoft Agent Framework 1.0?

Small teams should use Microsoft Agent Framework 1.0 only when they need durable agent behavior, tool orchestration, state, workflow control, or future enterprise integration. If the product feature is a single chat endpoint, summarizer, classifier, or deterministic automation, a direct SDK call will be cheaper and easier to operate. MAF is a good investment when the agent system is expected to grow beyond a single prompt path.

Quick Migration Verdict: Who Should Move Now and Who Should Wait

Semantic Kernel AutoGen migration is the process of moving existing Microsoft agent applications from Semantic Kernel or AutoGen into Microsoft Agent Framework 1.0, the production-ready .NET and Python foundation Microsoft announced in April 2026 with stable APIs and long-term-support positioning. Teams should move now when their agents are still close to prototype shape, when they already need MCP, DevUI, Azure AI Foundry hosting, checkpointing, or cross-language consistency, or when new work would otherwise deepen dependency on older abstractions. Teams should wait when the current system is business-critical, has weak regression tests, depends on custom AutoGen group chat behavior, or embeds Semantic Kernel plugins in ways that are not yet documented. The practical answer is not “rewrite everything”; it is “start with low-risk workloads, pin versions, and build migration evidence.” The clear takeaway: migrate deliberately, not reactively.

Microsoft has made the direction clear, but production teams still have to protect uptime. A customer-support triage agent with three tools and no persistent memory is a good first mover. A regulated approval agent that stores threaded state, calls internal APIs, and depends on exact streaming behavior needs a slower path. I would treat Agent Framework 1.0 as the default target for new Microsoft agent work and as a staged migration target for existing Semantic Kernel and AutoGen systems.

What Microsoft Agent Framework 1.0 Changes for Semantic Kernel and AutoGen Teams

Microsoft Agent Framework 1.0 is a unified SDK for building production-grade agents and multi-agent workflows in .NET and Python, created by the same Microsoft teams behind Semantic Kernel and AutoGen. In April 2026, Microsoft positioned it as production-ready with stable APIs, long-term-support commitment, MCP support, migration assistants, DevUI, Foundry integration, observability, evaluations, frontend streaming adapters, skills, and agent harness support. For Semantic Kernel teams, the main change is moving away from Kernel-centered construction toward agent and chat-client APIs built on Microsoft.Extensions.AI or the Python agent_framework package. For AutoGen teams, the main change is replacing AssistantAgent, model clients, group chat, and runtime patterns with Agent Framework equivalents. The key takeaway: Agent Framework consolidates the useful ideas from both projects behind a production-oriented surface.

The uncomfortable part is that “successor” does not mean “drop-in replacement.” Semantic Kernel encouraged a kernel-plus-plugin mental model. AutoGen encouraged conversational agents and group chat patterns. Agent Framework is trying to normalize agents, workflows, tools, state, durability, and hosting behind one model. That is better for long-lived systems, but it forces you to name behavior that may have been implicit.

Pre-Migration Inventory: Agents, Tools, State, Memory, Workflows, and Evals

Pre-migration inventory is the technical audit that records every agent, model, prompt, tool, state store, memory dependency, workflow, evaluation, and deployment boundary before code is moved. A real system can hide 30 or more tool contracts behind a single Semantic Kernel plugin folder or AutoGen agent registry, and one undocumented schema change can break production callers. Inventory should capture package versions, model providers, token settings, retry rules, streaming behavior, tool JSON schemas, vector indexes, human approval steps, logging fields, and rollback owners. It should also classify workloads by risk: stateless single-agent calls, stateful conversations, tool-heavy workflows, and multi-agent orchestration. The clear takeaway: a migration plan that lacks an inventory is just a rewrite with better branding.

I use a simple spreadsheet or YAML manifest for the first pass. Do not over-engineer it. The point is to expose coupling before it surprises you. For each agent, record the call path, owner, expected latency, failure mode, and tests. For each tool, capture inputs, outputs, side effects, auth scope, and idempotency. For each memory dependency, note whether data can be replayed, rebuilt, or must be preserved exactly.

What should the minimum migration inventory include?

A minimum migration inventory is a one-page-per-workload record that tells an engineer how the current agent behaves and how to prove the migrated version is equivalent. Include current framework, language, package version, model provider, prompt source, tools, state storage, streaming behavior, eval baseline, and rollback switch. If that document cannot explain what happens after a tool failure, the migration is not ready.

Semantic Kernel to Agent Framework Mapping: Packages, Agents, Sessions, Tools, and Invocation

Semantic Kernel to Agent Framework mapping is the translation from Kernel-centered objects, plugins, functions, threads, and invocation calls into Agent Framework packages, agents, sessions, tools, and chat clients. Microsoft documents Python installation as agent-framework with imports from agent_framework, while .NET projects can add packages such as Microsoft.Agents.AI; the June 2026 NuGet and PyPI listings show active post-1.0 iteration, so version pinning matters. Existing KernelFunction and vector-store investments do not have to be discarded because compatibility helpers and adapter patterns can expose them as Agent Framework tools. The migration is usually least risky when agents are recreated first, sessions are mapped second, and tool invocation is validated last. The clear takeaway: preserve proven Semantic Kernel assets, but stop making Kernel the center of new agent construction.

For .NET teams, the first mechanical step is usually package cleanup. Remove only what the migrated slice no longer needs, add the Agent Framework package, and keep old Semantic Kernel code behind an adapter until tests pass. For Python teams, separate provider-specific packages from broad framework dependencies once the migration stabilizes. Do not combine dependency cleanup with behavior changes unless you enjoy debugging three failures at once.

How should Semantic Kernel plugins move?

Semantic Kernel plugins should move as stable tool boundaries, not as copied implementation folders. Keep the plugin’s public function names, argument schema, validation, side effects, and error semantics stable while changing only the registration layer. When callers depend on exact names, preserve aliases during rollout. The migration is done when contract tests pass against both old and new registrations.

AutoGen to Agent Framework Mapping: Model Clients, AssistantAgent, Tools, GroupChat, and Runtime Patterns

AutoGen to Agent Framework mapping is the conversion of AutoGen model clients, AssistantAgent instances, tool functions, hosted tools, MCP integrations, group chats, middleware, custom agents, checkpointing, and human-in-the-loop behavior into Agent Framework equivalents. Microsoft’s official AutoGen guide describes Agent Framework as the new multi-language foundation built by core AutoGen and Semantic Kernel teams, which matters because AutoGen users are not being asked to abandon the agent design model entirely. The highest-risk mappings are group chat, custom runtime hooks, and conversation state; the lower-risk mappings are simple AssistantAgent workloads with plain tools and provider-backed model clients. Start by migrating one AssistantAgent that has deterministic tools and a known eval set. The clear takeaway: AutoGen migration is mostly about replacing runtime orchestration while preserving agent responsibilities.

AutoGen users should pay special attention to emergent behavior. If a GroupChat manager depended on conversational side effects, the new workflow must encode those rules explicitly. This is a good thing, but it changes the work. A reviewer-agent pattern, for example, should become a workflow step with clear inputs, outputs, retry rules, and approval criteria rather than a loose participant in a chat room.

What should happen to AutoGen GroupChat code?

AutoGen GroupChat code should usually become an explicit workflow, not a one-for-one chat clone. Identify each participant’s job, the turn-taking rule, the stopping condition, and the artifacts passed between agents. Then model those as workflow nodes and transitions. This reduces hidden behavior and makes replay, checkpointing, and observability easier to operate in production.

Migration Phase 1: Move Single-Agent Workloads First

Migration phase 1 is the controlled move of stateless or lightly stateful single-agent workloads from Semantic Kernel or AutoGen to Microsoft Agent Framework before touching orchestration, memory, or hosted runtime concerns. A good phase-1 candidate has one model provider, fewer than five tools, no business-critical writes, and a golden conversation set of at least 20 representative prompts. Microsoft says new Python projects can install Agent Framework with pip install agent-framework, and .NET projects can add Microsoft.Agents.AI, but dependency installation is the smallest part of the job. The real work is recreating the agent, preserving prompt behavior, mapping tool registration, and comparing outputs under fixed model settings. The clear takeaway: single-agent migration gives your team framework fluency before stateful systems are at risk.

The phase-1 rollout should run old and new paths side by side where possible. For user-facing workloads, route a small internal cohort to the new path first. For batch or backend agents, run shadow comparisons and log diffs. Expect some response variation even with the same model. Focus on task success, tool correctness, safety constraints, latency, and cost rather than exact prose.

What is a good first migration candidate?

A good first migration candidate is boring: one agent, one prompt family, a few read-only tools, clear success criteria, and an owner who can review output quality. Internal summarization, ticket classification, FAQ drafting, and research enrichment agents are usually better first candidates than payment, provisioning, or compliance agents. The goal is to learn the framework without risking expensive side effects.

Migration Phase 2: Port Tools, Function Schemas, Memory, and Vector Search

Migration phase 2 is the port of reusable tools, function schemas, memory access, and vector search from Semantic Kernel or AutoGen into Agent Framework-compatible boundaries. Tool migration deserves its own phase because Microsoft Agent Framework supports MCP and tool patterns, while existing systems may contain years of hidden assumptions about parameter names, auth scopes, null handling, and error strings. A single renamed argument can break an agent that learned to call customer_id, while a changed exception can bypass retry logic. Keep schema compatibility where possible, add adapter layers where needed, and test tools independently from agent prompts. Vector search should move through a stable retrieval tool or memory adapter before storage is replaced. The clear takeaway: tools are production contracts, not helper functions.

For each tool, write contract tests before changing registration. Test valid input, missing input, invalid enum values, auth failure, timeout, empty result, and side effects. If the tool writes data, make idempotency explicit. If the tool calls an internal API, decide whether the Agent Framework migration is allowed to change retry policy. Most incidents I have seen in agent migrations came from tool behavior drift, not model behavior.

Migration Phase 3: Replace Multi-Agent Chats with Agent Framework Workflows

Migration phase 3 is the redesign of AutoGen group chats, Semantic Kernel orchestration, and informal multi-agent loops as Agent Framework workflows with explicit nodes, transitions, stopping rules, and persisted artifacts. This phase is where many teams find that their old “multi-agent system” was actually a loosely controlled conversation with fragile success criteria. Agent Framework’s production-grade positioning includes graph-based patterns, durability, restartability, observability, governance, and human-in-the-loop control, which are hard to apply when orchestration is buried in chat transcripts. Start by naming each agent’s responsibility, input artifact, output artifact, retry policy, and escalation path. Then encode the collaboration as a workflow rather than preserving every conversational turn. The clear takeaway: workflow migration should make coordination auditable, not merely compatible.

A common pattern is planner, researcher, writer, reviewer. In AutoGen, those may have been participants in a GroupChat. In Agent Framework, the planner can emit a plan artifact, the researcher can enrich it, the writer can produce a draft, and the reviewer can approve or request a bounded revision. That structure is easier to checkpoint and much easier to explain during an incident review.

How do you preserve useful multi-agent behavior?

Useful multi-agent behavior is preserved by capturing decisions, not chat choreography. Keep role prompts, task boundaries, handoff artifacts, and quality gates. Drop accidental turn order, vague “continue” loops, and hidden dependence on one agent’s wording. If a debate improved quality, model it as two review passes with measurable acceptance criteria. Production workflows need replayable behavior more than theatrical conversation.

Migration Phase 4: Add Checkpointing, Human-in-the-Loop, Observability, and DevUI

Migration phase 4 is the production hardening stage where migrated agents gain checkpointing, human approval gates, observability, evaluations, and developer inspection tools such as DevUI. Microsoft lists these capabilities in the Agent Framework 1.0 feature set alongside Foundry integration, MCP, frontend streaming adapters, and migration assistants, which signals that the framework is aimed at operated systems rather than demos. Checkpointing lets long workflows resume after failure; human-in-the-loop protects high-impact actions; observability makes tool calls and model decisions inspectable; evaluations give release managers a baseline. This phase should define trace fields, approval events, eval suites, redaction rules, and dashboard ownership. The clear takeaway: the migration only becomes valuable when the new system is easier to operate than the old one.

Do not bolt on observability after launch. Add it while old and new systems still run side by side, because that is when you need comparisons most. Capture prompt version, model version, tool name, tool latency, retry count, workflow step, approval result, and final outcome. Redact payloads before they reach logs. Treat traces as production data with retention and access rules, not as a debugging convenience.

Where should human approval be required?

Human approval should be required wherever an agent can spend money, change customer data, send external messages, grant access, delete records, or make regulated decisions. Approval should pause a workflow with enough context for a reviewer to decide quickly: proposed action, evidence, confidence, alternatives, and risk. The approval event should be logged as a first-class workflow artifact.

Deployment Choices: Local Runtime, Azure AI Foundry, Durable Functions, and Cloud Hosting

Deployment choice is the decision about where migrated Agent Framework workloads run: local services, containerized application runtimes, Azure AI Foundry hosted agents, Durable Functions, or another cloud host. Microsoft Agent Framework 1.0 highlights Foundry hosting and production-oriented runtime capabilities, while the broader Microsoft agent adoption model asks teams to assess strategy, governance, architecture, operations, responsible AI, and value realization rather than only SDK fit. Local runtime is fastest for early migration and shadow tests. Foundry hosting can simplify managed agent deployment. Durable or workflow-oriented hosting is better for long-running, restartable processes. Existing cloud services remain valid when they already satisfy security and operational requirements. The clear takeaway: choose hosting based on workflow durability, governance, and ownership, not framework novelty.

For most teams, the first deployment should mirror the old runtime. If the Semantic Kernel service ran as a container behind an API, run the migrated Agent Framework slice the same way until behavior is proven. Move to hosted agents or durable workflow services when you need their operational features, not because migration created an excuse to change every layer.

Testing and Rollback Plan: Golden Conversations, Tool Contract Tests, Evals, and Version Pinning

A testing and rollback plan is the safety system that proves the Agent Framework migration preserves business behavior and can be disabled quickly if production metrics degrade. At minimum, use golden conversations, tool contract tests, retrieval tests, workflow scenario replays, eval suites, latency budgets, cost tracking, and version pinning. PyPI listed agent-framework 1.8.1 on June 9, 2026, and NuGet listed Microsoft.Agents.AI 1.10.0 updated on June 12, 2026, so teams should assume active iteration after 1.0 and lock dependencies for each release. Rollback should be a routing or feature-flag decision, not a frantic redeploy. The clear takeaway: migration confidence comes from repeatable evidence, not from a successful demo.

Golden conversations should cover happy paths, refusal paths, ambiguous input, tool failure, long context, and handoff cases. Tool contract tests should run without the model. Evals should measure task success and safety criteria. For rollback, keep the old path callable until the new path has passed at least one full release cycle and real production monitoring confirms parity.

What should a release gate include?

A release gate should include passing unit tests, tool contract tests, golden conversation review, eval threshold comparison, trace inspection, security review for changed permissions, load test for latency, and a documented rollback owner. For high-risk agents, require approval from the service owner and the business owner. The gate should be boring, repeatable, and visible in the release record.

Common Migration Traps and How to Avoid Them

Common migration traps are predictable failure modes that make Semantic Kernel AutoGen migration look complete while behavior quietly changes. The most frequent traps are treating migration as an import rename, changing prompts while changing frameworks, ignoring conversation state, assuming tool schemas are internal details, losing streaming semantics, overusing provider-specific features, skipping eval baselines, and migrating group chat without explicit workflow rules. Microsoft says Agent Framework simplifies the API surface and improves developer experience, but simplification can hide behavior you still need to preserve. Avoid these traps by freezing prompts during the first pass, pinning dependencies, writing contract tests, shadowing output, logging trace diffs, and migrating stateful flows after stateless flows. The clear takeaway: most migration failures are sequencing failures.

One subtle trap is “improving” the system while migrating it. That creates unreviewable diffs. If the old agent had a weak prompt, migrate the weak prompt first, prove equivalent behavior, then improve it in a separate release. Another trap is deleting Semantic Kernel or AutoGen compatibility code too early. Keep adapters until production evidence says they are no longer needed.

Final Checklist: A Practical 30-Day Migration Plan

A practical 30-day migration plan is a time-boxed sequence that moves one representative Semantic Kernel or AutoGen workload to Agent Framework 1.0 while building reusable patterns for the next migration. In the first week, inventory agents, tools, state, evals, owners, and rollback paths. In the second week, migrate one single-agent workload and run it against at least 20 golden conversations. In the third week, port shared tools with contract tests and add trace comparison. In the fourth week, shadow traffic, review evals, document rollback, and decide whether to migrate a stateful or multi-agent workload next. This plan fits teams that already have CI and production ownership; slower organizations should extend the timeline rather than skip controls. The clear takeaway: 30 days should produce one proven migration pattern, not a rushed portfolio rewrite.

Use the first migrated workload as a template. Capture the package versions, adapter patterns, test fixtures, trace fields, deployment steps, and release checklist. Make the second migration faster because the first one created evidence, not because the team lowered standards. If leadership wants every agent moved immediately, show the risk matrix and ask which rollback guarantees they are willing to remove. That usually clarifies the real priority.

What does “done” mean for the first migration?

The first migration is done when the Agent Framework path passes tests, matches eval thresholds, runs in the target environment, emits useful traces, has a rollback switch, and has an owner for post-release monitoring. It is not done when the code compiles. The real finish line is operational confidence under production-like conditions.

FAQ: Semantic Kernel, AutoGen, Microsoft Agent Framework, MCP, A2A, and Foundry

FAQ for Semantic Kernel AutoGen migration refers to the short answers teams need before committing roadmap time, especially around whether Agent Framework replaces older Microsoft agent SDKs, how quickly production systems should move, and what happens to MCP, A2A, Azure AI Foundry, and existing Semantic Kernel or AutoGen code. Microsoft Agent Framework 1.0 was announced for .NET and Python in April 2026 with migration assistants for both Semantic Kernel and AutoGen, but the practical migration path still depends on your current architecture. The safest default is to start new Microsoft agent work on Agent Framework, migrate simple existing agents first, and postpone complex stateful or multi-agent systems until tests, tracing, checkpointing, and rollback are ready. The clear takeaway: Agent Framework is the forward path, but your migration schedule should be governed by risk.

Is Microsoft Agent Framework a replacement for Semantic Kernel and AutoGen?

Microsoft Agent Framework is Microsoft’s forward production foundation for agent applications, and it incorporates lessons from both Semantic Kernel and AutoGen. For new work, it should usually be the default. For existing work, replacement should be staged. Semantic Kernel plugins, AutoGen tools, and proven orchestration ideas can often be adapted rather than discarded.

Do I need to migrate all Semantic Kernel code immediately?

You do not need to migrate all Semantic Kernel code immediately. Start with new projects and low-risk agents, then move production systems after inventory, tests, and rollback controls exist. Existing KernelFunction and vector-search investments can often be exposed through adapters, which lets you reduce risk while changing the agent runtime.

What is the hardest part of AutoGen migration?

The hardest part of AutoGen migration is usually group chat behavior, not AssistantAgent construction. Simple AssistantAgent workloads map cleanly compared with multi-agent conversations that depend on turn order, implicit stopping rules, or custom runtime hooks. Convert those systems into explicit workflows with named artifacts, transitions, and evaluation criteria.

Should MCP be part of the first migration?

MCP should be part of the first migration only when the current workload already needs external tool interoperability or when tool boundaries are the main reason for moving. If the first goal is framework parity, keep MCP out until the migrated agent behaves correctly. Add MCP after tool contracts and auth scopes are stable.

How does Azure AI Foundry fit into the migration?

Azure AI Foundry fits as a hosting and management option for Agent Framework workloads, especially when teams want managed agent deployment and Microsoft cloud integration. It is not required for every migration. Keep the first deployment close to the old runtime unless Foundry solves a specific operational problem such as hosting, governance, or integration.