AI agent checkpoints: how to resume unfinished work

A checkpoint is a compact record of an agent's unfinished work that lets a later run continue safely. It should preserve the objective, completed actions, evidence and next step. A conversation summary alone may omit the exact state needed to avoid repeating an action.

Save state at meaningful boundaries

Imagine an assistant assembling a report from several departments. It has retrieved two approved documents and requested a third. A useful checkpoint identifies the versions already read, the missing source and the location of the partial report. It does not simply say research is in progress.

Save after a verified step or before handing work to another owner. Saving every thought adds noise; saving only at the end loses the value of recovery.

Include the minimum resumable record

• Objective and scope: the result still required and the limits on action.
• Completed work: artifacts or destination records, with identifiers.
• Pending work: missing inputs, unresolved decisions and the next step.
• Side effects: actions already attempted and any unknown outcomes.
• Ownership: who may continue and which approvals remain valid.

Treat the checkpoint as a lead to evidence. Before resuming, verify that the referenced documents still exist and that a previous approval still covers the proposed action. A stale snapshot cannot authorize a new commitment.

Test a restart deliberately

Interrupt a test run after it creates a draft. Restart from the checkpoint and observe whether the system finds the existing draft or creates another. Then change a source document between runs. The agent should notice the version change or make the uncertainty visible.

This is a practical way to discover hidden reliance on temporary memory. A workflow that succeeds only in one uninterrupted conversation may not be ready for ordinary interruptions.

Make the next action specific

Write retrieve the current inventory file and reconcile item IDs rather than continue the project. Include a link to the partial artifact and a brief explanation of what remains unverified. Good checkpoints reduce repeated work for both the agent and the human who eventually takes over.

Be Human editorial guidance. Examples are illustrative unless explicitly attributed. Sources provide background; implementation choices depend on your workflow.