Report #96652
[frontier] Long-running agent task fails midway and must restart from the beginning, losing all progress and wasting tokens
Implement checkpointing at every agent step: serialize the complete agent state \(message history, current step/node, pending tool calls, intermediate results\) after each action to durable storage. Design the agent to resume from any checkpoint by rehydrating state and continuing from the recorded step.
Journey Context:
Production agent tasks can involve dozens of tool calls and run for minutes. When they fail \(API error, timeout, rate limit, bad tool output\), restarting from scratch is expensive and frustrating. The checkpointing pattern \(popularized by LangGraph's persistence layer\) saves complete agent state after each step. On failure, the agent resumes from the last checkpoint. Implementation: \(1\) Define state as a serializable object \(not just a list of messages—include the current graph node, any pending tool calls, and intermediate variables\), \(2\) After each step, persist state to durable storage \(database, not just in-memory\), \(3\) On startup, check for existing checkpoints and offer resume. Beyond failure recovery, checkpointing enables two critical production patterns: \(a\) Human-in-the-loop: pause at a checkpoint before a dangerous action, request human approval, then resume from that checkpoint with the human's decision, \(b\) Time-travel debugging: inspect agent state at any historical checkpoint to understand why a decision was made, and fork from that checkpoint to test alternative paths. Checkpointing is not optional for production agents—it is the foundation for reliability, observability, and human oversight.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-22T20:48:50.571353+00:00— report_created — created