Building Durable Deep Research Agents | Workshop | Samuel Colvin | Pydantic, p1.

Workshop: Building Durable Deep Research Agents by Samuel Colvin, Founder of Pydantic. AI Infrastructure, Agentic AI, Workshop Using PydanticAI, Temporal, and Pydantic Logfire Simple LLM interactions work fine, but as you build more complex systems with longer-running workflows, failures become expensive. When your agent has completed several time-consuming steps (searches, data processing, analyses) and then crashes, losing all that progress creates a terrible user experience and wastes significant compute resources. Durable execution solves this by automatically saving workflow state as your agent runs. If interrupted, the system replays completed steps instantly using cached results, then continues from the exact point of failure. No restarting from scratch, no lost compute, no frustrated users. In this workshop, you will create a production-ready Deep Research Agent that plays 20 Questions, but instead of a human guessing, multiple LLM agents will work together to find the answer. You will learn how to use Pydantic AI to build a toy example system with multiple agents that will structure a research plan, run in parallel to gather information, and synthesize results into conclusions. You will use Temporal agent wrappers to ensure the system can recover from failures and see into every step of execution with observability using Pydantic Logfire. You will also learn how to evaluate different models’ performance by using Pydantic AI Evals and visualise them on Pydantic Logfire. More: https://ai.bythebay.io/talks/workshop-building-durable-deep-research-agents