Microsoft Agent Framework 1.0 GA: Semantic Kernel and AutoGen Merge Into One Stack
Microsoft ships Agent Framework 1.0 with LTS support, unifying Semantic Kernel and AutoGen into a single framework with native A2A and MCP protocol support.
Microsoft ended the confusion. After months of developers asking “Should I use Semantic Kernel or AutoGen?”, the answer is now “neither — use Microsoft Agent Framework.” Version 1.0 GA ships with Long-Term Support, unifies both predecessor frameworks, supports .NET and Python, and includes native A2A and MCP protocol support out of the box. This is Microsoft declaring its standard stack for enterprise agent development.
What Happened
Microsoft released Agent Framework 1.0 as Generally Available in April 2026, marking the convergence of two previously separate projects:
Semantic Kernel was Microsoft’s SDK for building AI-powered applications. It focused on orchestrating LLM calls with plugins, planners, and memory systems. It was production-grade, well-documented, and widely adopted in enterprise .NET shops.
AutoGen was Microsoft Research’s framework for multi-agent conversations. It excelled at defining agent roles, managing inter-agent communication, and orchestrating complex workflows where multiple AI agents collaborate. It was research-oriented, Python-first, and popular in the AI research community.
The problem was clear: developers building agent systems needed capabilities from both frameworks. Semantic Kernel had the production infrastructure; AutoGen had the multi-agent orchestration. Choosing one meant compromising on the other, and attempting to combine them introduced integration complexity that neither framework was designed for.
Agent Framework 1.0 merges these capabilities into a single framework with these key properties:
| Property | Detail |
|---|---|
| Languages | .NET and Python (first-class support for both) |
| Support | Long-Term Support (LTS) commitment |
| Protocols | Native A2A (Agent-to-Agent) and MCP (Model Context Protocol) |
| Model support | Compatible with all model providers (OpenAI, Anthropic, Google, local models) |
| Core capability | Multi-agent workflows, orchestration, tool invocation |
What the Unified API Looks Like
The framework provides a single programming model for defining agents, giving them tools, and orchestrating their interactions. An agent can be backed by any LLM provider. Tool invocation uses MCP natively, meaning any MCP-compatible tool server works without adapter code. Agent communication uses A2A, enabling agents built with Microsoft’s framework to communicate with agents built on other frameworks that support the same protocol.
This is model-agnostic, protocol-native, and language-flexible — the three properties that enterprise developers care about most.
Why It Matters
The Enterprise Agent Stack Crystallizes
Before Agent Framework 1.0, building an enterprise agent system on Microsoft’s stack required assembling pieces: Semantic Kernel for LLM orchestration, possibly AutoGen for multi-agent patterns, custom code for protocol support, and integration glue to connect everything. Each piece had its own versioning, documentation, and community.
Now there is one framework, one set of documentation, one versioning scheme, and one LTS guarantee. For enterprise architects who need to justify technology choices to leadership, this simplification is decisive. “We use Microsoft Agent Framework” is a sentence that procurement, legal, and management can understand. “We use Semantic Kernel for orchestration, AutoGen for multi-agent patterns, custom MCP adapters, and a home-grown A2A implementation” is not.
Native Protocol Support Is the Real Story
The inclusion of native A2A and MCP support is more significant than the framework unification itself. Here is why:
MCP support means that any tool or service that exposes an MCP interface can be used by agents built with this framework without writing adapter code. The MCP ecosystem is growing rapidly — databases, APIs, file systems, code repositories, and cloud services are all publishing MCP servers. Native support means agents get access to this entire ecosystem for free.
A2A support means agents built with Microsoft’s framework can communicate with agents built on other platforms. This is critical for enterprise environments where different teams or departments may use different AI platforms. An agent built on Microsoft’s stack can coordinate with an agent built on LangGraph, CrewAI, or any other A2A-compatible framework.
Together, these protocol integrations position the framework as an interoperable node in a larger agent ecosystem rather than a walled garden.
.NET Gets First-Class Agent Development
The AI agent development space has been disproportionately Python-centric. LangChain, LangGraph, CrewAI, AutoGen (original) — all Python-first. This left .NET developers either wrapping Python libraries awkwardly or building agent patterns from scratch.
Agent Framework 1.0 makes .NET a first-class citizen for agent development. For the massive enterprise .NET ecosystem — banking, healthcare, government, manufacturing — this removes the language barrier to adopting agent architectures. Teams can build agents in the same language as their existing systems, using the same tooling, CI/CD pipelines, and deployment infrastructure.
LTS Changes the Adoption Calculus
The LTS commitment is not just a label. Enterprise software procurement requires stability guarantees. When Microsoft commits to long-term support, it means security patches, bug fixes, and backward compatibility for a defined period. This lets enterprises build on the framework with confidence that it will not be deprecated or radically changed in 18 months.
Compare this to the agent framework landscape of 2024-2025, where frameworks appeared, gained popularity, introduced breaking changes, and sometimes went dormant within months. LTS is Microsoft saying “this is the long-term bet.”
What To Do About It
1. If You Are on Semantic Kernel or AutoGen, Plan Your Migration
Microsoft has published migration guides for both Semantic Kernel and AutoGen users. The core concepts carry over — agents, tools, memory, orchestration — but the API surface has changed. Start with a small proof-of-concept migration of your simplest agent to understand the new patterns before migrating production systems.
2. Evaluate the .NET Path for Enterprise Agents
If your organization is .NET-based and has been using Python for agent development as a workaround, evaluate whether Agent Framework 1.0 lets you consolidate. Running agents in the same runtime as your application reduces operational complexity, simplifies deployment, and lets you share types and business logic between your application and your agents.
3. Build MCP Servers for Your Internal Tools
With native MCP support in the framework, any internal tool or service you expose as an MCP server becomes immediately available to your agents. Prioritize high-value internal services: databases, internal APIs, monitoring systems, deployment pipelines. Each MCP server you create expands what your agents can do without writing agent-specific integration code.
4. Test A2A Interoperability Early
If your organization uses multiple AI platforms, set up a proof-of-concept where agents on different frameworks communicate via A2A. Identify the friction points early: message format differences, capability negotiation, error handling across framework boundaries. Understanding these issues now prevents surprises when you scale to production multi-agent systems.
5. Lock to the LTS Version
When adopting Agent Framework 1.0, explicitly pin to the LTS release channel. Do not track preview or nightly builds for production systems. The LTS guarantee is one of the framework’s strongest selling points — use it. Establish an update cadence that follows LTS patch releases without chasing feature releases.
Microsoft’s unification of Semantic Kernel and AutoGen into Agent Framework 1.0 is the kind of consolidation that happens when a technology category matures past the experimentation phase. The framework is not revolutionary in its capabilities — most of what it offers was possible by combining its predecessors. What is new is the coherence: one framework, two languages, standard protocols, and long-term support. For enterprise development teams, that coherence is often more valuable than novelty.
Sources
- Visual Studio Magazine: Microsoft Agent Framework 1.0 GA (April 2026)
- Microsoft DevBlogs: Agent Framework Announcement (April 2026)