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What Is Durable Execution for AI Agents?

Durable execution lets an AI agent pause, survive crashes or deploys, and resume mid-workflow by checkpointing each step — essential when agents wait on humans or slow systems.

2 min readUpdated durable-execution.md

Durable execution is the ability for an agent workflow to survive interruptions — crashes, deploys, timeouts, or long human waits — and resume from the last completed step instead of starting over.

Real agents don't finish in one request. They wait on approvals, poll slow APIs, and run for hours or days. Without durability, a server restart mid-task means lost context and duplicated side effects.

How it usually works

  1. The runtime checkpoints progress after each meaningful step
  2. State (messages, tool results, pending approvals) is stored durably
  3. If the process dies, a new worker replays or resumes from the checkpoint
  4. Side effects are designed to be idempotent so retries don't double-charge or double-send

Frameworks and workflow engines (Eve, Temporal, LangGraph persistence, and peers) provide these primitives so you don't hand-roll recovery.

Why blueprints should call it out

Durability is an architecture requirement, not a nice-to-have. If your agent needs human approval before refunding, or waits overnight for a batch job, the blueprint should say so — and the stack must support pause/resume without burning compute or forgetting state.

See Human-in-the-Loop for approval pauses, and Agent Orchestration for the coordination layer that owns retries and state.

Blueprint

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