Curious what people see in these frameworks in 202(6). My experience has been that an agent is a simple while loop over tools/instructions/dialog. More complex integrations generally lie in the tools/context retrieval - but those have so far been so domain specific that it’s not worth pulling in a framework.
You can do specialized SLMs with different roles working on problems. Also deterministic workflows. That is what I gathered its use. I know last year, multi-agent scenarios were topping to benchmarks but I don't know if 2025 has been the same.
I remember when "Microsoft Agent" meant the APIs that gave rise to Clippy, Rover, and (regrettably) even Bonzi Buddy.
The bitter irony is, Microsoft has since embrace-extend-extinguished Bonzi Buddy spyware tech, building it right into Windows 11. So... they're moving onward to the future I guess?
I have used this in a “beta” feature for an enterprise app and really like it. In ~100 lines of code I have a secured OpenAI compatible endpoint that I can chat with, and write tools for in .NET. I have it doing natural language query over some data and it works quite well.
You can also expose the agents as MCP, AGUI and so it can be a tool you integrate with other AI platforms.
I'm on the Python advocacy team at Microsoft, so I've been experimenting a bit with the new framework. It works pretty well, and is comparable to Langchainv1 and Pydantic-AI, but has tighter integrations with Microsoft-specific technologies. All the frameworks have very similar Agent() interfaces as well as graph-based approaches (Workflow, Langgraph, Graph).
I can flesh that out if it's helpful. I find it fascinating to see where agent frameworks converge and diverge. Generally, the frameworks are converging, which is great for developers, since we can learn a concept in one framework and apply it to another, but there are definitely differences as you get into the edge cases and production-level sophistication.
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[ 5.2 ms ] story [ 31.4 ms ] thread> It brings together and extends ideas from Semantic Kernel and AutoGen projects, combining their strengths while adding new capabilities
… and giving a hint what will happen to the aforementioned projects.
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It’s a shame when someone’s promotion is tied to how many new things they ship.
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> To learn more about migrating from either Semantic Kernel or AutoGen, see the Migration Guide from Semantic Kernel and Migration Guide from AutoGen.
It seems the motto of Microsoft for the last 15 years: “You won’t have time for new features — all you’ll do is migrations.”
Said nobody?
The bitter irony is, Microsoft has since embrace-extend-extinguished Bonzi Buddy spyware tech, building it right into Windows 11. So... they're moving onward to the future I guess?
You can also expose the agents as MCP, AGUI and so it can be a tool you integrate with other AI platforms.
I have a repository here with similar examples across all those frameworks: https://github.com/Azure-Samples/python-ai-agent-frameworks-...
I started comparing their features in more details in a gist, but it's WIP: https://gist.github.com/pamelafox/c6318cb5d367731ce7ec01340e...
I can flesh that out if it's helpful. I find it fascinating to see where agent frameworks converge and diverge. Generally, the frameworks are converging, which is great for developers, since we can learn a concept in one framework and apply it to another, but there are definitely differences as you get into the edge cases and production-level sophistication.