Durability

Agents run for minutes or hours, call slow tools and sometimes wait for a human. If the process dies halfway, the run is gone and you start over, sometimes repeating actions that already happened.

In this workshop we use Temporal to make a long-running agent durable, so it survives failures and resumes where it stopped.

What we'll cover: - Why long-running agent runs fail in production: crashes, timeouts, restarts, retries - Checkpointing agent state so a run can resume from another process - Pausing for human approval and resuming later - Making side effects idempotent so a resumed run doesn't do things twice - LangGraph checkpointing as an alternative, and when a plain loop is still enough

You'll leave knowing how to make your own agent runs survive failures.

Who it's for: people running agents that take real actions or run for a long time.

Prerequisites: Python, an LLM API key, a basic agent with tools. Our Temporal workshop is related. The code and recording are shared with members after the session.