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...

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...

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, ...

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. ...

When people talk about building AI agents, they usually focus on models, tools, and prompts. In practice, one of the most important architectural decisions is much simpler: where does the conversation history live? Imagine a user asks your agent a complex question, clicks “try again,” explores two different answers in parallel, and then comes ba...

We’re excited to announce the release of Microsoft.Extensions.VectorData.Abstractions, a foundational library providing exchange types and abstractions for vector stores when working with vector data in AI-powered applications. This release is the result of a close collaboration between the Semantic Kernel and .NET teams, combining expertise in AI ...

Today we’re thrilled to announce support for Hybrid search with Semantic Kernel Vector Stores for .NET. What is Hybrid Search? Hybrid search performs two parallel searches on a vector database.  The union of the results of these two searches are then returned to callers with a combined rank, based on the rankings from each of the constituent sear...