My team is currently using AutoGen, but we find the non-deterministic nature of agent chats hard to debug. Would switching to Pydantic AI give us more control over the specific logic and data types passed between agents?
3 answers
The switch from AutoGen to Pydantic AI usually happens when a team prioritizes "control" over "autonomy." AutoGen is great for open-ended brainstorming between agents, but it can be unpredictable in production. Pydantic AI treats agents as high-level Python objects with strictly defined inputs and outputs. Instead of agents just "chatting" until they hopefully finish, you define exact hand-off points and validation schemas. This results in a much more stable and predictable system. If you need your AI to follow a rigorous business process where every step must be verified, the Pydantic-first approach will significantly reduce your debugging time and increase your deployment confidence.
Does Pydantic AI support the same level of code-execution sandbox features that AutoGen is famous for?
It feels much more like writing standard enterprise software than playing with a research project.
That’s exactly why we moved. The type hints and structured outputs made it feel like we were finally building real software with LLMs.
It doesn't have the same "out-of-the-box" Docker execution environment as AutoGen, but you can easily wrap a tool in a Docker container yourself. It gives you the "primitives" rather than a pre-packaged opinionated sandbox.