Microsoft Agent Framework 1.0: AutoGen and Semantic Kernel Are Now One Production SDK

Microsoft released Agent Framework 1.0 on April 6, 2026 — a production-ready SDK that consolidates AutoGen and Semantic Kernel into a single framework for .NET and Python. MAF 1.0 ships with A2A (Agent-to-Agent) and MCP dual protocol support, multi-agent orchestration primitives, and explicit model-provider agnosticism.

This is the formal graduation of AutoGen from research project to enterprise infrastructure.

What MAF 1.0 Actually Ships

The framework consolidation is more than a namespace merge. AutoGen was always the multi-agent coordination layer, Semantic Kernel the tool-use and memory abstraction. Separately, they addressed overlapping concerns in ways that created integration friction. MAF 1.0 provides a unified surface:

• Orchestrator/Worker topology built-in, with configurable agent roles and message routing
• A2A protocol support for agent-to-agent communication across process boundaries
• MCP support for tool integration — any MCP server can expose tools to MAF agents
• Provider-agnostic LLM binding — swap Claude, GPT-4, Llama, or custom models without architecture changes
• State management with pluggable backends (in-memory, Redis, durable storage)

The production-ready designation matters specifically for .NET shops. Microsoft’s enterprise customer base runs overwhelmingly on .NET, and those customers have been waiting for an AI agent framework they can actually deploy in a regulated, enterprise environment. MAF 1.0 ships with the security, observability, and deployment patterns that enterprise engineering teams require.

A2A + MCP: The Dual-Protocol Advantage

MAF 1.0 supporting both A2A and MCP puts it in a unique position among agent frameworks. A2A handles agent-to-agent communication — the coordination layer for multi-agent systems where one agent delegates subtasks to another. MCP handles agent-to-tool communication — how agents invoke external capabilities like databases, APIs, and file systems.

Together, they cover the full connectivity surface of a multi-agent system:

• Vertical integration (agent → tool) via MCP
• Horizontal coordination (agent ↔ agent) via A2A

LangGraph and CrewAI have their own coordination mechanisms, but neither ships with production-grade A2A + MCP support as defaults. This is a meaningful technical differentiation.

The 40% Gartner Number

Gartner projects that 40% of enterprise applications will embed AI agents by the end of 2026, up from under 5% in 2025. This eight-fold increase in adoption creates a real market for production-grade agent infrastructure — which is exactly what MAF 1.0 is positioning for.

The enterprises reaching for agent frameworks in 2026 are not early adopters doing experiments. They’re engineering teams with production requirements: audit logs, access controls, deterministic failure modes, SLA commitments. AutoGen in its experimental form couldn’t meet those requirements. MAF 1.0 is built to.

For Python Developers: MAF vs LangGraph vs CrewAI

The Python ecosystem now has three credible production options:

Dimension	MAF 1.0	LangGraph	CrewAI
Orchestration model	Configurable	DAG-based	Role-based
A2A protocol	Native	Manual	Manual
MCP support	Native	Plugin	Plugin
Enterprise support	Microsoft backing	LangChain	Community
.NET support	Yes	No	No

If you’re building multi-agent systems that need to span .NET and Python environments, or if you’re in a Microsoft-aligned enterprise stack, MAF 1.0 is the obvious choice. If you’re Python-only with complex DAG requirements, LangGraph remains competitive. If your use case is role-assigned agent teams, CrewAI is still a simpler starting point.

The practical advice: unless you have strong reasons to prefer another framework, MAF 1.0’s A2A + MCP defaults are the best foundation for new enterprise agent projects starting today.

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Source: Microsoft Agent Framework 1.0 — Release Blog