Prototyping an AI agent is easy. Running one in production is not. Once an agent does real work chaining LLM calls, waiting on flaky APIs, running for minutes or hours it's a stateful distributed system, and it fails in distributed-system ways:
- The worker process crashes or gets redeployed mid-task
- An LLM call hits a rate limit or times out
- The run needs to pause for a human, then resume
A run that's 80% done is suddenly gone, and you're restarting it by hand or burning tokens re-running everything.
Why "just add retries" isn't enough
A retry loop protects a single call inside a process that's still alive. It does nothing when the process itself dies. What you actually need is for the whole workflow to survive a crash and resume where it left off — a guarantee called durable execution.
What Temporal does
Temporal is an open-source workflow engine built on two ideas:
- Event History — every step is recorded as an event in a durable log on the server, not held in process memory. Each LLM call and tool call becomes a persisted event.
- Replay — on restart, Temporal re-runs your code against that log, skips already-completed activities (reusing saved results instead of re-calling the LLM), and resumes from the exact point of failure. You describe what should happen; the engine handles how it survives failure.
This is battle-tested infrastructure, not a toy
Think of it like a game's autosave: power cuts mid-level, you respawn at the last checkpoint with your inventory intact — you don't replay the whole game.
Netflix runs their Spinnaker deploy platform on this. Their old system kept state in memory, so a crash mid-operation lost the deployment. After moving to Temporal, transient deployment failures dropped from 4% to 0.0001% — and they deleted years of homegrown retry logic.
That same durable-execution foundation now underpins production AI: OpenAI co-built an official Agents SDK integration on it, and Codex and Replit's agent run on it.
I'll run an agent through a multi-step task (research → summarise → verify → report), kill the worker mid-run on stage, restart it, and show it resume at the exact next step — no repeated LLM calls, no manual restart.
