Building Production-Ready AI Agents with Pydantic AI

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Oct 7, 2025

Samuel Colvin, the mastermind behind the Pydantic validation library, shares his journey in creating Pydantic AI—a type-safe framework for AI agents in Python. He discusses the importance of stability and observability, comparing single-agent versus multi-agent systems. Samuel explores architectural patterns, emphasizing minimal abstractions and robust engineering practices. He also addresses code safety and the challenge of model-provider churn, while promoting open standards for enhanced observability. Join him as he reveals insights on crafting reliable AI agents!

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INSIGHT

FastAPI-Level Engineering For Agents

Pydantic AI aims to bring FastAPI-like engineering quality to agent frameworks with strong typing and minimal abstractions.
The project prioritizes reliability, observability, and avoiding breaking changes while keeping opinions light.

INSIGHT

Agents As Modular Building Blocks

The modern agent is a building block, not always the top-level construct; many applications use multiple agents collaborating.
Single-agent loops remain useful but often sit inside orchestrators or multi-agent systems.

ADVICE

Use Existing Orchestration Patterns

Reuse existing engineering practices like orchestration instead of inventing entirely new AI paradigms.
Call agents as functions or tools inside orchestrators to compose complex workflows safely and simply.

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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?

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