Showing category results for Agent Framework

Vector stores have been part of the Python experience in Semantic Kernel for some time. With Microsoft Agent Framework, we have ported that work forward rather than starting over. This post focuses on Python. For the .NET equivalent, see Use vector stores in .NET AI apps. The foundation remains familiar: typed record definitions, st...

Foundry hosted agent isolation exposes two independent controls. User isolation identifies whose data may be used. At the same time, Foundry hosted session isolation identifies where code and files continue to live. For each control, Foundry can resolve the value or the application can provide it. Therefore, the available choices support direct ...

Keep your domain services distributed. Move the specialist's instructions, not another model, into the orchestrator. A multi-agent system often starts with a straightforward design: one agent understands the user's request, delegates to specialist agents, and combines their answers. That was the starting point for my ski resort demo. A resort...

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...