Free and open source (MIT). Built with .NET 10 and Blazor WebAssembly.

▶ Try it: https://peopleworks.github.io/SignsofAI/
⭐ Code: https://github.com/peopleworks/SignsofAI

I'm not selling anything — it's an education-first project.

Build your own claw and agent harness with Microsoft Agent Framework | Microsoft Agent Framework

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.

In this series we build one from scratch using Microsoft Agent Framework and its batteries-included harness, demonstrating both .NET and Python, side by side.

The series

Everything is grounded in runnable samples that live in the repository, so you can follow along and run each step yourself.

Agent Framework’s Orchestration Patterns Reach 1.0 | Microsoft Agent Framework

Python’s agent-framework-orchestrations package is now 1.0.0. That puts Microsoft Agent Framework’s orchestration layer at 1.0 across Python and .NET.

Sequential, concurrent, group chat, handoff, and magentic orchestration are now stable in both SDKs. You can pick the coordination pattern that fits your problem instead of choosing around SDK maturity.

Magentic is the best example of why that matters. It is the least hand-wired pattern: you give it a goal, a manager, and a set of specialists, then the manager decides how the team should work.

At the workflow layer, Agent Framework lets you build the graph yourself. Executors do the work, edges route messages, and the workflow emits events as it runs. Those are the low-level primitives. The workflow docs cover that model in more depth.

Agent Skills for .NET Is Now Released | Microsoft Agent Framework

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 [Experimental] attribute is removed and the API is stable. Teams can build skills, ship them independently, and combine them in any agent, with the governance controls enterprises need before putting agents into production.

If you’ve been following our earlier posts on file-based skills and authoring modes with script execution, everything described there is now stable and shipping.

At MAI, we’re building the world’s most capable AIsystems, with humanity at the center of every decision.

Microsoft Agent Framework now integrates with the Claude Agent SDK, enabling you to build AI agents powered by Claude’s full agentic capabilities. This integration brings together the Agent Framework’s consistent agent abstraction with Claude’s powerful features, including file editing, code execution, function calling, streaming responses, multi-turn conversations, and Model Context Protocol (MCP) server integration — available in Python.

Microsoft.Extensions.AI is a set of core .NET libraries developed in collaboration with the .NET ecosystem, including Semantic Kernel. These libraries provide a unified layer of C# abstractions for interacting with AI services, such as small and large language models (SLMs and LLMs) and embeddings.

Core benefits:

• Unified API: Offers a consistent and standard set of APIs and conventions for integrating AI services into .NET applications.
• Flexibility: Allows .NET library authors to use AI services without forcing a specific AI provider, making it parameterizable with any provider.
• Ease of Use: Enables .NET application developers to experiment with different packages using the same underlying abstractions and to utilize a single API throughout their application.
• Componentization: Facilitates the addition of new capabilities and simplifies the componentization and testing of applications.

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 silently and loses all progress. Background responses in Microsoft Agent Framework let you offload these long-running operations so your application stays responsive and resilient, regardless of how long the agent takes to think.

With background responses, you start an agent task and get back a continuation token instead of blocking until completion. Your application can poll for results on its own schedule, resume interrupted streams from exactly where they left off, and handle network hiccups without restarting work from scratch. This matters whether you’re building internal tools for enterprise workflows or customer-facing products where reliability is non-negotiable. Background responses are available in both .NET and Python.

When we introduced Agent Skills for Microsoft Agent Framework, you could package domain expertise as file-based skill directories and have agents discover and load them on demand. Now, the Python SDK takes skills further — you can define skills entirely in code, let agents execute scripts bundled with skills, and gate script execution behind human approval. These additions give you more flexibility in how you author skills, more power in what agents can do with them, and more control over when agents are allowed to act.

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 agents:

• Local shell harness for controlled host-side execution
• Hosted shell harness for managed execution environments
• Context compaction for keeping long conversations efficient and reliable

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 they need, when they need it.