Showing category results for .NET

Your agents can now be built on a stable, batteries-included harness - the loop, planning, memory, context management, approvals, and telemetry that turn a model into an agent that actually does things - in both Python and .NET. What is an agent harness? An agent harness is the scaffolding that turns a language model into an agent. A model on its...

You can now give your .NET agents reusable packages of domain expertise - instructions, reference documents, and scripts they load only when a task needs them - through a stable, production-ready API. Agent Skills for .NET in Microsoft Agent Framework has moved out of experimental preview - the attribute is removed and the API is stable. Teams can...

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 ...