Showing category results for Python

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, ...

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 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. ...

Python developers working with Agent Skills can now author skills as files on disk, as inline Python code, or as reusable classes - and mix them freely through composable source classes that handle discovery, filtering, and deduplication. A skill living in your local repository, one installed from your organization's internal package index, and...

Prompt injection is the #1 risk on the OWASP LLM Top 10, and most agents in production today defend against it with one of two heuristics: a defensive system prompt, or a hand-rolled allowlist. Neither is deterministic. Both fail silently the day someone slips a line into an issue body, an email, or a tool result. With FIDES (Flow Integri...

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...