Semantic Kernel vs AutoGen vs Microsoft Agent Framework

Semantic Kernel launched first, as a lightweight SDK for composing 'plugins' (functions, prompts, and connectors) around LLM calls with enterprise concerns — dependency injection, telemetry, and multi-language support (C#, Python, Java) — built in from day one. It was designed for teams that wanted LLM orchestration to feel like normal enterprise software engineering: typed, testable, and instrumentable.

AutoGen emerged from Microsoft Research as an experimentation-first framework for multi-agent conversation patterns: multiple agents (each with a role, a system prompt, and optionally tool access) converse with each other to solve a task, with patterns like group chat, hierarchical delegation, and human-in-the-loop checkpoints. AutoGen's strength was rapid research-style prototyping of novel agent-to-agent interaction patterns, and it built a large open-source community around exactly that flexibility.

Having two frameworks with overlapping but philosophically different goals — Semantic Kernel's engineering rigor versus AutoGen's flexible multi-agent experimentation — created real confusion for enterprise teams trying to pick one for production. Microsoft's answer is the Microsoft Agent Framework, announced as the convergence of Semantic Kernel's enterprise-grade plugin/connector model and AutoGen's multi-agent orchestration patterns into a single, actively developed SDK — effectively the successor most new projects should target, with Semantic Kernel's core abstractions carried forward and AutoGen's multi-agent patterns reimplemented on top of the same production-grade foundation.

This kind of framework consolidation is not unusual in Microsoft's developer-tools history — similar convergences have happened before as research prototypes matured into production platforms — but it does mean architects evaluating 'Semantic Kernel vs AutoGen' as a static, permanent choice are asking a slightly outdated question. The more useful question for a project starting today is: does Agent Framework already cover my needs, or is there a specific capability gap (a connector, a language SDK, a community integration) that still makes one of its two predecessors the better near-term choice, with a deliberate plan to reassess as Agent Framework matures.

Semantic Kernel's core abstraction is the 'kernel' — a runtime that holds your configured model connections, registered plugins (native code functions or semantic/prompt functions), and memory/planner components — around which you build a single agent's reasoning loop with strong typing and dependency-injection-friendly patterns familiar to .NET and enterprise Python teams. It supports function calling natively across the major model providers, has mature connectors for Azure AI Search and vector databases for RAG, and — critically for enterprises already running production .NET or Java services — first-class multi-language SDK parity, not a Python-only afterthought.

Semantic Kernel added an 'Agent Framework' extension (predating and distinct from the standalone Microsoft Agent Framework) for basic multi-agent group-chat patterns, but it was always secondary to its core single-agent-plus-plugins model. If your team's primary need is 'wrap an LLM in production-grade orchestration with strong typing, telemetry, and enterprise DI patterns, calling out to tools and retrieval,' Semantic Kernel remains a solid, actively supported choice — and is the safest incremental step for teams already invested in it, since Microsoft has committed to continued support and a migration path toward Agent Framework rather than an abrupt cutoff.

AutoGen's defining contribution was making multi-agent conversation a first-class primitive: define several agents with distinct roles (a planner, a coder, a critic, a human proxy), let them converse in a structured group chat or hierarchical pattern, and let the framework manage turn-taking, termination conditions, and tool invocation within that conversation. This unlocked patterns — like a 'critic' agent reviewing a 'coder' agent's output before it's accepted — that are awkward to express in a single-agent plugin model.

AutoGen's tradeoffs, historically, were less mature production tooling (observability, typed contracts, enterprise identity integration) compared to Semantic Kernel, and a faster-moving API surface driven by its research origins, which made some enterprise teams hesitant to commit to it for long-lived production systems. It remains an excellent choice for rapid prototyping of novel multi-agent interaction patterns, research-oriented teams, and proof-of-concept work where you need to validate whether a multi-agent approach even solves your problem before investing in production hardening.

As of the Microsoft Agent Framework's release, new production multi-agent projects should generally target Agent Framework rather than AutoGen directly, since Agent Framework absorbs AutoGen's core multi-agent orchestration patterns (group chat, hierarchical delegation) while adding the production-grade tooling Semantic Kernel contributed — but AutoGen remains a reasonable choice for fast, throwaway prototyping where framework longevity is not a concern.

Microsoft Agent Framework is positioned as the unification of Semantic Kernel's production-grade engineering foundation (typed connectors, telemetry, enterprise identity integration, multi-language support) with AutoGen's multi-agent orchestration patterns (group chat, hierarchical delegation, human-in-the-loop), delivered as a single actively developed SDK with first-class integration into Azure AI Foundry's Agent Service for hosting, observability, and deployment. For teams starting a new agentic project today, this is generally the recommended starting point rather than choosing between the two predecessor frameworks.

Agent Framework's practical advantages: a single mental model for both single-agent-plus-tools and multi-agent-conversation architectures (so you don't have to re-platform if a project grows from one agent to a coordinated team of specialist agents), native deployment onto Azure AI Foundry's managed Agent Service runtime for production hosting, observability, and versioning, and a clearer long-term support commitment from Microsoft than either of the two frameworks it succeeds carried individually.

The tradeoff is maturity: as the newest of the three, Agent Framework has a smaller body of production case studies, community Stack Overflow answers, and third-party integrations than Semantic Kernel has accumulated over several years. Teams with an existing, working Semantic Kernel investment should evaluate migration deliberately (Microsoft has published migration guidance) rather than rewriting reflexively, while teams starting greenfield should default to Agent Framework unless a specific missing integration forces a different choice.

A practical way to de-risk adopting a newer framework is to pilot it on a lower-stakes internal use case first — an internal knowledge-base assistant or an engineering-team productivity agent, for example — before committing a customer-facing or revenue-critical workflow to it. This lets your team build operational familiarity (deployment patterns, monitoring, incident response) with Agent Framework's specific tooling while the ecosystem itself continues to mature, rather than learning both a new framework and handling a production incident on a critical workflow simultaneously.

The table below summarizes the practical decision factors we walk enterprise clients through during architecture discovery. Treat 'maturity' as a moving target — Agent Framework's ecosystem is growing quickly, and this comparison should be revisited each time you kick off a new project rather than relied on as a permanent snapshot.

Beyond the summary table, it's worth stress-testing your specific requirements against each framework's actual documentation and sample repositories rather than relying solely on a comparison article like this one — frameworks in this space evolve quickly, and the most reliable way to validate fit is a short proof-of-concept spike (typically one to two weeks) implementing your single hardest orchestration requirement in your top candidate framework before committing to a full build.

A support triage agent is a good Semantic Kernel fit when the core loop is predictable: classify the ticket, retrieve policy context, call a case-management API, and draft a response for review. The engineering value is in typed tool contracts, deterministic fallbacks, and telemetry around each plugin call, not in letting several autonomous agents debate the next action. For this pattern, a single well-instrumented agent with strong retrieval and human approval gates is usually safer and cheaper than a multi-agent conversation.

A complex proposal-generation workflow is a stronger Agent Framework candidate. One specialist agent can inspect the account context, another can map solution architecture, a third can draft the commercial narrative, and a reviewer agent can enforce brand, compliance, and pricing-policy constraints before a human sees the output. The important distinction is that these are not independent chatbots; they are coordinated roles with explicit handoffs, shared state, traceable decisions, and clear stop conditions. That orchestration model is exactly what a converged framework should standardize for production teams.

AutoGen still has a place when the question is exploratory rather than operational. If a research team is testing whether a planner-critic-coder pattern improves solution quality, or whether human checkpoints should happen after every tool call versus only at phase boundaries, AutoGen's flexible conversation primitives make that experiment quick. Once the pattern graduates from experiment to repeatable business process, rebuild the winning shape in Agent Framework or a hardened Semantic Kernel architecture so observability, authentication, deployment, and regression testing are treated as first-class production concerns.

The most common Semantic Kernel failure mode is over-customizing planners until a straightforward application becomes a hidden agent platform. If your design needs dynamic delegation among many personas, emergent debate, or evolving team composition, forcing that into plugin calls and prompt templates can create brittle orchestration that only one senior engineer understands. In that case, move the coordination layer to Agent Framework and keep Semantic Kernel-style plugins as the typed tools the agents call.

The most common AutoGen failure mode is letting a successful demo become production architecture by inertia. Multi-agent transcripts are impressive, but without strict termination rules, budget caps, role boundaries, and trace-to-ticket observability, a conversation can loop, call tools unnecessarily, or produce an answer that is difficult to explain during an incident review. Do not use AutoGen directly for regulated or customer-facing workflows unless you have deliberately built the missing production controls around it.

The most common Agent Framework failure mode is adopting it just because it is the strategic direction, before your team has validated the specific SDK, hosting, and integration surfaces your workload needs. Newer frameworks can have rough edges in samples, community answers, and third-party connectors. If a mission-critical release depends on a mature connector that only Semantic Kernel supports today, it can be better to ship the stable path now with a written migration trigger than to block delivery waiting for framework parity.

There is also a broader anti-pattern: using any agent framework when a normal workflow engine, rules service, or search application would solve the problem more reliably. If the task has fixed inputs, deterministic branching, and little need for language understanding or tool-selection judgment, start with conventional software. Add an agent only where language reasoning, ambiguous user intent, or flexible tool composition creates business value that a simpler design cannot provide.

If you have a production Semantic Kernel deployment today, do not treat Agent Framework's release as an urgent forcing function — Microsoft has committed to continued Semantic Kernel support and has published incremental migration guidance, so the responsible path is to evaluate migration as part of your normal roadmap planning (e.g., when the next major feature requires multi-agent orchestration Semantic Kernel's own agent extension doesn't handle cleanly), not as an emergency rewrite.

If you have production AutoGen deployments, the calculus is different: AutoGen's long-term investment from Microsoft is explicitly being redirected toward Agent Framework, so teams with meaningful AutoGen production surface area should start planning a migration path now, prioritized by which agents are most business-critical, rather than waiting for a support cliff. In both cases, we recommend a phased migration — build new agent capabilities directly in Agent Framework, and migrate existing agents opportunistically as they need meaningful changes anyway — rather than a big-bang rewrite of a working system.

A practical migration playbook we use with clients: inventory every production agent and its underlying framework, rank them by business criticality and remaining useful life, and migrate the highest-criticality, longest-lived agents first (since they carry the most risk if the source framework's support eventually lapses) while leaving low-criticality or soon-to-be-retired agents on their current framework until they're naturally replaced. This risk-weighted approach avoids both extremes — an expensive big-bang migration of everything at once, or an indefinite deferral that leaves your most important agents on a framework receiving shrinking investment.

Regardless of which framework you choose, production agent systems need a testing and evaluation discipline that goes well beyond typical unit testing, because LLM outputs are non-deterministic and a prompt or model-version change can silently regress behavior that passed every existing test. Semantic Kernel's mature ecosystem includes well-documented patterns for integrating with Azure AI Foundry's evaluation SDK and Application Insights telemetry, giving teams a relatively well-trodden path to production-grade observability with several years of community-contributed patterns to draw on.

AutoGen's multi-agent conversation logs are naturally rich in diagnostic detail — since agents converse explicitly, you can inspect the full transcript of how a task was decomposed and solved — but turning that transcript into structured, automated regression tests historically required more custom tooling than Semantic Kernel's more typed, function-call-oriented approach. Microsoft Agent Framework is designed to close this gap by natively integrating with Azure AI Foundry's Agent Service tracing and evaluation tooling regardless of whether you're running a single agent or a multi-agent conversation pattern, which is one of the strongest arguments for defaulting new projects to it: you get one consistent observability story instead of stitching together different tooling for single-agent and multi-agent parts of your system.

Whichever framework you choose, budget explicitly for building a 'golden set' of representative test scenarios with expected behavior, and re-run that evaluation suite whenever you change a prompt, swap a model version, or modify tool definitions — this is the single highest-leverage practice for catching regressions before they reach production, and it's a practice that pays for itself regardless of which of the three frameworks ultimately underlies your agent.

Framework selection should also account for who is actually going to write and maintain the code. Teams with a strong .NET or enterprise Java background will find Semantic Kernel's typed, dependency-injection-friendly API more natural to adopt and code-review than Python-first alternatives, while teams with a Python-and-data-science-heavy composition (common in ML-adjacent product teams) may find AutoGen's or Agent Framework's Python SDK a more comfortable starting point regardless of the specific orchestration pattern needed.

For organizations building a dedicated AI platform or center-of-excellence team responsible for agent infrastructure across many business units, standardizing on Microsoft Agent Framework as the single supported framework — rather than allowing every team to independently pick Semantic Kernel, AutoGen, or a competing open-source framework — pays real dividends in shared tooling, shared observability dashboards, and transferable engineering expertise across projects, and is generally the recommendation we make to clients building this kind of central platform capability today.

It's also worth building a lightweight internal decision record whenever a team chooses a framework other than the organizational default — documenting the specific capability gap that justified the exception — so that as Agent Framework matures and closes those gaps, there's a clear, low-friction trigger for revisiting the decision rather than an exception silently becoming permanent by default. This kind of lightweight governance is far less overhead than it sounds and pays for itself the first time a new hire asks 'why does this one service still use AutoGen directly.'