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Part 2 of Build your own claw and agent harness with Microsoft Agent Framework. In Part 1 we stood up a harness and gave our personal finance assistant its first abilities: a custom tool, web search, and planning. It can talk about the markets - but it can't yet touch your data, and nothing stops it from taking a sensitive action on a whim. T...

Part 1 of Build your own claw and agent harness with Microsoft Agent Framework. In the overview we said a "claw" is really just an agent harness: a loop around a model, wired up with tools, planning, memory, and more. In this first post we stand up that loop and give our personal finance assistant its first three abilities: plan...

What does it take to build your own "claw" - a capable, CLI-style agent that can plan, use tools, remember things, and safely act on your behalf? Coding agents and assistants like these can feel like magic, but underneath they are an agent harness: a loop around a language model wired up with tools, planning, memory, approvals, and observability. ...

Building powerful AI agents is only half the story, running them safely in production is the real challenge. As customers adopt Microsoft Agent Framework for agent orchestration, a clear need has emerged for robust, built-in governance. In this post, Imran Siddique from the AGT team walks through how Agent Governance Toolkit pairs with Agent Framew...

A Tour of the Handoff Orchestration Pattern Most multi-agent systems start out simple: a router agent receives a user request, picks the right specialist, and forwards the conversation. As long as each specialist can complete its task in one pass, that model works fine. The first time it breaks is when an agent needs more: a follow-up questio...

As organizations move from single-agent prototypes to multi-agent production systems, the ability for agents to communicate reliably across platforms and organizational boundaries becomes essential. With the release of A2A Protocol v1.0 and updated support in the Microsoft Agent Framework, you can now connect and expose your AI agents using a stabl...

Your agents can now draw on skills authored in three different ways – as files on disk, as inline C# code, or as encapsulated classes – and combine them freely in a single provider. Add built-in script execution support and a human-approval mechanism for script calls, and you have a practical authoring model that fits real-world scenarios: skills t...

Handling Long-Running Operations with Background Responses AI agents powered by reasoning models can take minutes to work through complex problems — deep research, multi-step analysis, lengthy content generation. In a traditional request-response pattern, that means your client sits idle waiting for a connection that may time out, or worse, fails ...

Agent harness is the layer where model reasoning connects to real execution: shell and filesystem access, approval flows, and context management across long-running sessions. With Agent Framework, these patterns can now be built consistently in both Python and .NET. In this post, we’ll look at three practical building blocks for production agent...

You can now equip your Microsoft Agent Framework agents with portable, reusable skill packages that provide domain expertise on demand — without changing a single line of your agent's core instructions. With built-in skills providers for both .NET and Python, your agents can discover and load Agent Skills at runtime, pulling in only the context the...