Managing Model-Provider Churn and Unification

Summary
In this episode of the AI Engineering Podcast Samuel Colvin, creator of Pydantic and founder of Pydantic Inc, talks about Pydantic AI - a type-safe framework for building structured AI agents in Python. Samuel explains why he built Pydantic AI to bring FastAPI-like ergonomics and production-grade engineering to agents, focusing on strong typing, minimal abstractions, and reliability, observability, and stability. He explores the evolving agent ecosystem, patterns for single vs. many agents, graphs vs. durable execution, and how Pydantic AI approaches structured I/O, tool calling, and MCP with type safety in mind. Samuel also shares insights on design trade-offs, model-provider churn, schema unification, safe code execution, security gaps, and the importance of open standards and OpenTelemetry for observability.

Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
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• Your host is Tobias Macey and today I'm interviewing Samuel Colvin about the Pydantic AI framework for building structured AI agents

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what Pydantic AI is and the story behind it?
• What are the core use cases and capabilities that you are focusing on with PydanticAI?
• The agent SDK landscape has been incredibly crowded and volatile since the introduction of LangChain and LlamaIndex. Can you give your summary of the current state of the ecosystem?
  • What are the broad categories that you use when evaluating the various frameworks?
• Beyond the volatility of the frameworks, there is also a rapid pace of evolution in the different styles/patterns of agents. What are the patterns and integrations that Pydantic AI is best suited for?
• Can you describe the overall design/architecture of the Pydantic AI framework?
  • How have the design and scope evolved since you first started working on it?
• For someone who wants to build a sophisticated, production-ready AI agent with Pydantic AI, what is your recommended path from idea to deployment?
  • What are the elements of the framework that help engineers across those different stages of the lifecycle?
• What are some of the key learnings that you gained from all of your efforts on Pydantic that have been most helpful in developing and promoting Pydantic AI?
• What are some of the new and exciting failure modes that agentic applications introduce as compared to web/mobile/scientific/etc. applications?
• What are the most interesting, innovative, or unexpected ways that you have seen Pydantic AI used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pydantic AI?
• When is Pydantic AI the wrong choice?
• What do you have planned for the future of Pydantic AI?

Contact Info

• GitHub
• LinkedIn

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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Links

• Pydantic
• Pydantic AI
• Pydantic Inc
• Pydantic Logfire
• OpenAI Agents
• Google ADK
• LangChain
• LlamaIndex
• CrewAI
• Durable Execution
• Temporal
• MCP == Model Context Protocol
• Claude Code
• Typescript
• Gemini Structured Output
• OpenAI Structured Output
• Dottxt Outlines SDK
• smolagents
• LiteLLM
• OpenRouter
• OpenAI Responses API
• FastAPI
• SQLModel
• AI SDK JavaScript
• LangGraph
• NextJS
• Pyodide
• AI Elements frontend component library

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0