How would you recover a long-running agent after a process failure?

Checkpoints authoritative state, reconciles ambiguous tool outcomes, preserves approvals, and resumes without repeating completed work.

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TL;DR

I persist the run goal, current state, completed steps, tool requests and receipts, approvals, budgets, and terminal status at durable boundaries. On restart, the runner loads that state and continues only unfinished work. Tools use idempotency keys or reconciliation queries so an ambiguous timeout does not repeat a side effect.

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Practice focus

Durable execution, checkpointing, idempotency, reconciliation, approval persistence, and resume semantics.

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Core answer
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Overview
Recovery means resuming from known state, not replaying the entire conversation and hoping the model repeats itself.
I persist the run goal, current state, completed steps, tool requests and receipts, approvals, budgets, and terminal status at durable boundaries. On restart, the runner loads that state and continues only unfinished work. Tools use idempotency keys or reconciliation queries so an ambiguous timeout does not repeat a side effect.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
Checkpointing occurs before and after consequential operations and whenever the run pauses for a person or external event. In-flight work has explicit unknown outcomes that must be resolved before replanning. Model-generated summaries help rebuild context but do not replace the authoritative event and state record. Resume verifies that tool schemas, permissions, model policy, and approvals are still valid.

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Details

Replaying the full agent loop from the beginning.
Treating timed-out actions as failed without reconciliation.
Resetting step and cost limits after restart.

How would you recover a long-running agent after a process failure?

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