Durable execution: why long-running agents need a workflow engine
Sample story. What happens to a 40-step agent run when the process dies at step 31, and how engines like Temporal make it resume instead of restart.
TL;DR
- Durable execution persists each step's result so a crashed run can be replayed to the same state and continued.
- Workflow code must be deterministic; anything non-deterministic (LLM calls, HTTP, clocks) goes in activities whose results are recorded.
- Activities should be idempotent, because a step that crashed mid-flight may run again.
The save-game analogy#
Think of a long video game with autosave. Without it, a power cut at the final boss sends you back to the title screen. With it, you reload and carry on from the last checkpoint. Durable execution is autosave for code: every meaningful step is written down, so a crash costs you seconds, not the whole run.
Agent runs are long games. A research agent might make dozens of LLM and tool calls over several minutes. Restarting from scratch after a crash wastes money, repeats side effects (two emails sent, two tickets filed) and frustrates users.
How a resume works#
What this sample leaves out#
A full story would cover workflow determinism rules, activity retries and timeouts, how LangGraph checkpointers compare with Temporal's event history, and the cost of replaying very long histories. This sample stops here.
You are reading the overview. Switch to “Full deep dive” in the top bar for the mechanics, maths and code.
Why this matters to you
You already run Temporal next to LangGraph at HivePro. Knowing exactly what replay does (and why workflow code must be deterministic) is the difference between a resilient agent platform and mysterious non-determinism errors in production.
Sources
- Temporal Workflow · Temporal docs
- Durable execution · LangGraph docs