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You can pause an agent mid-task and resume it four minutes later with its context intact—but four minutes is not a special retention window or a guarantee. The reliable approach is to identify what “memory” means in your setup, preserve that state in the right place, and resume using the same session, thread, or saved run state. Conversation history, a pending approval, workspace files, and an application’s record of completed actions are separate kinds of state.
First identify what needs to survive
Before pausing, decide which of these the agent will need to continue correctly:
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- Conversation history: messages and prior turns.
- Interrupted run state: the paused execution, including a pending approval or tool call.
- Thread objective: a durable task goal attached to a particular thread.
- Workspace state: files, snapshots, and reusable memory stored outside the conversation.
- Application task state: the status of real operations, such as whether a request was already submitted.
These may be stored in different places. Preserving one does not automatically preserve the others.
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Keep the session identity and storage stable
The Agents SDK session layer loads stored conversation history before a run and saves new items afterward. To continue that history, resume with the same session instance, or configure another instance with the same session ID and underlying storage backend. OpenAI’s Sessions documentation specifically calls for this when a run pauses for approval.
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Restore the interrupted run after an approval
Conversation history alone may not be enough when execution stopped at a human-approval step. The SDK surfaces pending approvals as interruptions; its RunState can serialize the paused run so the application can resume it after the interruption is approved or rejected. The flow can apply to tools called by the main agent, a handoff, or nested Agent.as_tool() execution. If the application also uses sessions, retain the same session identity and backing store while restoring the run. See the Human-in-the-loop documentation.
Codex: resume a Goal in its original thread
For a Codex Goal, the documented controls are /goal pause and /goal resume. Goals are persisted state attached to the current thread, not global memory or project-level instructions. Continue in the thread where the Goal lives; a fresh, unrelated thread should not be assumed to have the same context. The Codex cookbook says Goals are available starting in Codex 0.128.0, but availability and commands can change, so check the current Goals documentation for your build.
Sandbox agents: preserve history, memory, and workspace separately
OpenAI’s sandbox guidance distinguishes SDK-managed sessions, which preserve message history, from sandbox memory, which stores distilled, reusable lessons in files. It also describes continuing a live sandbox session, resuming session state, starting from a snapshot, or mounting persistent storage to reuse a memory directory. A workspace snapshot or persistent directory can preserve files without making those files equivalent to the conversation history. Choose the mechanism that matches the state you need, as described in the Sandbox Agents guide.
GPT-Live: restore context and reconcile actions
The GPT-Live guide describes continuing after a session ends by forking a completed stored recording on a new connection, when storage is enabled, permitted by policy, and finalization succeeded. If there is no completed recording to fork, start a new session with relevant saved text history. Keep the session ID and application task state so the new connection can restore both context and the task’s status.
Before retrying an unfinished action, check its status in the application backend. A lost or restarted conversation does not prove the action failed; repeating it without checking can duplicate a submission. The guide says stored recordings are available for 30 days and that Zero Data Retention treats storage as false, making forking unavailable. These are API-specific details; verify current behavior and policy in the GPT-Live session guide.
Choose the continuation method by state and storage
| What must survive | Where it is kept | How to continue |
|---|---|---|
| Conversation messages | SDK session store | Reuse the session instance or match its session ID and backing storage. |
| Paused approval or execution | Serialized run state | Restore the saved run state after resolving the interruption; retain matching session storage if sessions are enabled. |
| Codex Goal | Current thread | Use /goal pause and /goal resume in the Goal’s thread. |
| Sandbox files or reusable memory | Live sandbox, snapshot, or persistent storage | Resume the session, restore a snapshot, or reconnect the persistent storage appropriate to the workspace. |
| External operation status | Application backend | Check the operation’s current status before deciding whether to retry. |
A practical pause-and-resume checklist
- Record the continuation key. Save the session ID, thread, or other identifier required by your implementation.
- Save the right state. Persist conversation history, serialized run state, workspace files, or application task status as applicable; do not assume one contains the others.
- Resolve interruptions deliberately. If a tool is awaiting approval, record the pending decision and resume the interrupted run only after the decision is made.
- Restore the same context. Reconnect to the matching session and storage, original thread, or saved sandbox state.
- Reconcile side effects. Check backend status before repeating an operation that may already have completed.
The four-minute gap does not change these requirements. Continuity depends on how the agent’s system stores and restores state, not on the length of this particular pause.
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