# Microsoft Ships Agent Framework 1.0 Merging Semantic Kernel and AutoGen

> Microsoft has shipped Agent Framework 1.0 for both .NET and Python, merging two of its previously separate efforts: the enterprise-focused Semantic Kernel and the multi-agent orchestration of AutoGen. The result is a single SDK meant to be the default way to build agents on Microsoft's stack, and it arrived alongside a busy stretch for the ecosystem, with Pydantic AI V2 and LlamaIndex Workflows 1.0 both reaching stable in the same 48-hour window. The release also leans on interoperability. The Agent-to-Agent protocol has crossed 150 adopting organizations and is now natively integrated across Azure AI Foundry, AWS Bedrock, and Google Cloud, a sign that the industry is converging on shared plumbing for how agents talk to one another. For developers, consolidation like this is mostly welcome. The past year produced a confusing sprawl of overlapping frameworks, and folding Semantic Kernel and AutoGen into one supported path reduces the risk of betting on a dead-end library. The broader story is that agent engineering is maturing from experimental scripts into something with stable SDKs, versioned releases, and cross-cloud standards, the unglamorous infrastructure that has to exist before agents can be trusted in production.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: LangChain · Published Friday, July 10, 2026_

## Wortins' read

Microsoft has shipped Agent Framework 1.0 for both .NET and Python, merging two of its previously separate efforts: the enterprise-focused Semantic Kernel and the multi-agent orchestration of AutoGen. The result is a single SDK meant to be the default way to build agents on Microsoft's stack, and it arrived alongside a busy stretch for the ecosystem, with Pydantic AI V2 and LlamaIndex Workflows 1.0 both reaching stable in the same 48-hour window. The release also leans on interoperability. The Agent-to-Agent protocol has crossed 150 adopting organizations and is now natively integrated across Azure AI Foundry, AWS Bedrock, and Google Cloud, a sign that the industry is converging on shared plumbing for how agents talk to one another. For developers, consolidation like this is mostly welcome. The past year produced a confusing sprawl of overlapping frameworks, and folding Semantic Kernel and AutoGen into one supported path reduces the risk of betting on a dead-end library. The broader story is that agent engineering is maturing from experimental scripts into something with stable SDKs, versioned releases, and cross-cloud standards, the unglamorous infrastructure that has to exist before agents can be trusted in production.

## Source

[Read the full story at LangChain](https://www.langchain.com/state-of-agent-engineering)

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