A resumable agent run is an AI agent execution that can be paused, serialized, and continued later without starting over. It preserves enough state to recover the active agent, conversation items, tool results, approvals, usage, and any workspace state needed for the task.
The ability to resume matters for long-horizon tasks. A task may be interrupted by human approval, tool latency, rate limits, budget caps, process restarts, deployments, or a user returning hours later.
State can be stored at several levels:
- conversation history or session items;
- model-provider continuation identifiers;
- current agent and turn count;
- pending tool calls or approval requests;
- serialized application context;
- agent sandbox session state or snapshots; and
- trace identifiers and usage records.
A resumable run is different from agent memory. Memory stores durable knowledge for future tasks. Resumable run state stores the operational state of a specific in-progress task.
The key engineering challenge is consistency. If a tool call changed external state before the run paused, resuming must not accidentally repeat the operation. Idempotency keys, durable action logs, and explicit completion markers are important.
Serialization should be conservative. Custom objects, credentials, open file handles, and live network connections may not round-trip safely. The system should record enough metadata to rebuild the required context without storing secrets unnecessarily.
Resumable runs also improve human-in-the-loop workflows. An agent can stop at an approval boundary, persist its state, and continue after the user or reviewer responds.
The OpenAI Agents SDK RunState reference describes a serializable snapshot used to continue interrupted runs, including model responses, generated items, approval state, and optional conversation identifiers.
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