Showing archive results for 2026

Agents are more useful when they can remember what matters beyond the current conversation. Today, we're announcing a new preview integration that gives Microsoft Agent Framework agents durable, cross-session memory backed by Azure Cosmos DB. The new Python package, , provides . Attach it to an agent once and it can automatically store conversat...

Part 4 of Build your own claw and agent harness with Microsoft Agent Framework. Over the last three parts our personal finance assistant grew from a single tool into a genuinely capable agent: it plans, reads your portfolio, asks before it trades, remembers what matters, loads skills on demand, reorganizes files with a shell, computes with CodeA...

An agent or workflow is only useful when people and other systems can reach it through the interfaces and channels they already use. That might be an OpenAI Responses client, Telegram, another agent using A2A, or an MCP client. As a builder of agents and workflow, you need control over which channels you expose it to and how it behaves. To help, ...

Developers increasingly want to build agents that can reason about code, modify files, execute commands, interact with developer tools, and work across entire repositories. While GitHub Copilot already provides a powerful coding harness for these scenarios, developers often need additional capabilities such as observability, middleware, approval wo...

Microsoft Agent Framework supports creating agents that use the GitHub Copilot SDK as their backend. GitHub Copilot agents provide access to powerful coding-oriented AI capabilities, including shell command execution, file operations, URL fetching, Model Context Protocol (MCP) server integration, and can be integrated into your existing development...

Your agents can now discover and load Agent Skills directly from a Model Context Protocol (MCP) server. Instead of shipping every skill inside your application or copying skill folders into each deployment, you point an agent at an MCP server and it pulls the skills it needs on demand. A central team can publish skills once, and every agent across ...

Most multi-agent apps wire every flow in application code: the sequence of steps, branching, and handoffs between agents all live inside the program, making the orchestration harder to review, version, and change. Declarative workflows make that orchestration explicit. In YAML, you define how agents coordinate, how state changes, where exe...

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...

Your Python agents can now pick up reusable bundles of domain expertise (instructions, reference material, and scripts that load only when a task calls for them) through a stable, production-ready API. Agent Skills for Python in Microsoft Agent Framework is stable and shipping: the core skills API has no experimental gate, so you can rely on it...

Part 3 of Build your own claw and harness with Microsoft Agent Framework. In Part 2 our personal finance assistant learned to work with your data safely: it reads your portfolio, asks before it trades, and remembers what matters across sessions. It's useful - but everything it knows is baked into one prompt, it does its work one step at a time, ...