Agent Resumption
Persist agent execution state so long-running tasks survive restarts, deploys, and user disconnects without losing progress.
Intent & Description
🎯 Intent
Persist agent execution state so a multi-hour run survives restarts, deploys, or user disconnects.
📋 Context
Production agents that take minutes or hours to finish — scraping large datasets, running multi-step migrations — will inevitably hit worker restarts, host failures, or session drops. Throwing away in-flight work is unacceptable to both operators and users.
💡 Solution
Two battle-tested approaches. (a) Deterministic replay (Temporal/Inngest pattern): state = inputs + log of side-effects; on resume, re-execute the workflow code and skip effects that already have logged results. (b) Checkpoint snapshots (LangGraph Cloud pattern): periodically serialize plan, working memory, partial outputs, and pending tool calls; restore on restart. Both require idempotency keys passed to side-effect targets so a replayed-but-unlogged call deduplicates downstream — without this, crash-between-effect-and-log produces duplicates.
Real-world Use Case
- Agent runs are long enough that restarts, deploys, or disconnects would lose meaningful work.
- Side effects can be logged or snapshotted without breaking semantics on replay.
- Users or operators need confidence that in-flight runs survive infrastructure events.
Source
📌 TL;DR
Serialize your agent state at checkpoints so crashes and deploys are a brief pause, not a full restart — your users will never know the difference.
Advantages
- Dramatically improves reliability for long-running agents.
- Deploys no longer kill user work mid-flight.
Disadvantages
- Checkpoint storage adds cost.
- Resumed runs may encounter drifted external state.
- Deterministic replay requires workflow code to be deterministic — any non-determinism corrupts resume.
- Tools without idempotency key support cannot be safely replayed.