Most AI agent frameworks share a quiet assumption: the process will stay alive. Set off a multi-step research agent, and the code assumes the LLM API will respond, the network will behave, and your machine will keep running until the job finishes. In practice, none of those hold for jobs that run for hours or days. Temporal is built for exactly that gap. It is a durable execution platform that serializes every step of a workflow into an event log. If the worker process crashes, network partitions, or an LLM rate-limit kicks in, Temporal restores state and resumes execution from the last completed step — no lost progress, no orphaned agent loops. This guide covers what Temporal is, why it matters specifically for long-running AI workflows, and how to structure a Python-based durable agent today. Effloow Lab installed temporalio 1.27.0 , validated the Workflow + Activity + Signal pattern locally, and documented what works and what requires a running server.…