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Multi-turn agent recovery is the process of restoring enough conversation and workflow state to continue an AI agent’s work safely after an interruption. Depending on the system, that can mean resuming a paused run, carrying a conversation into a later turn, or restarting a durable workflow from a checkpoint. Those are related, but they are not the same operation: each has a different state owner and recovery scope.
What multi-turn agent recovery means—and why it matters
An agent run may involve more than a model response. It can call tools, hand work to another agent, wait for human approval, or update an external system. If the process stops while that work is underway, simply sending the last user message again may not restore the original situation. The agent could lose its place, repeat a completed action, or continue without an approval or tool result it still needs.
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Recovery means continuing from a known state: identifying what was completed, what remains pending, and what must happen next. The goal is not merely to make another request. It is to restore the right context and execution status while avoiding unsafe replay of work that may already have taken effect.
The phrase “multi-turn” can refer to the history that informs later user turns, while “recovery” can refer to an interrupted execution. Keep those meanings separate. Resuming a run picks up an existing execution; continuing a conversation starts a later turn using an established history strategy. A system can support one without automatically supporting the other.
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Three different ways an agent can continue
Agent frameworks and APIs place state in different locations. Before designing recovery, identify which component owns the state and what that state represents.
| Mechanism | State owner | What it continues | Key consideration |
|---|---|---|---|
| Application-managed history | Your application | A later conversation turn, using history the application supplies again | The application must preserve and replay the correct conversation input; this is not necessarily a paused-run resume. |
| SDK-managed session | An SDK session backend | A conversation that continues across turns | Use the same relevant session and backend when recovering a write, and account for the history already committed. |
| Server-managed conversation or response chain | The service, referenced by a conversation ID or previous response ID | A subsequent turn with prior context managed through that reference | Send the new turn according to that continuation method; it does not by itself mean an interrupted tool loop is resumed. |
| Serialized run state | The application or SDK holding the saved run state | An interrupted execution, including its progress and any pending items represented in that state | The restored state must match the agent graph and execution context that can interpret it. |
| Workflow checkpoint | The workflow runtime or persistence layer | A workflow restarted from a saved checkpoint | Checkpointing supports later resumption, but does not itself prove that external effects ran exactly once. |
The OpenAI Agents SDK reference distinguishes application replay through to_input_list(), SDK-managed sessions, server-managed conversation_id, response chaining with previous_response_id, and resuming a run from RunState. Its guidance is to choose one conversation-state owner for an ordinary continuation path rather than accidentally combining strategies. A saved RunState answers a different question from a conversation ID: the first represents interrupted execution; the second identifies context for a later turn.
What state may need to survive an interruption
There is no universal agent-recovery schema. What must be saved depends on the framework and on the work the agent is allowed to perform. Treat the following as design categories, not a promise that every SDK persists each field.
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- Conversation identity and history: the session or conversation reference and the exact history known to have been committed. If the application owns history, it needs a reliable way to reconstruct the input for the next call.
- Execution progress: the interrupted run state, active agent, workflow position, staged input, and any pending tool output or handoff that the framework represents.
- Approval state: which action is awaiting a decision and the approval or rejection associated with that pending item.
- Effect status: whether a provider request, tool call, or external update may already have succeeded. A restart that cannot distinguish “not done” from “done but response lost” risks duplicating work.
- Compatibility context: the framework version, session backend, and agent graph needed to load and interpret the saved state.
Save enough information to make a recovery decision, but do not assume that serializing a conversation transcript also serializes an agent’s pending execution. A transcript can preserve what was said while omitting the tool-loop position or approval state needed to resume the run.
A safe recovery sequence
Recovery should be a controlled decision, not an automatic replay of the most recent request. A practical sequence is:
- Classify the interruption. Determine whether the process failed during a model request, while saving session history, after a tool action, while waiting for approval, or at a workflow boundary. The failure point narrows what may have completed.
- Identify the state owner. Decide whether this path uses application-managed history, an SDK session, a server-managed conversation or response reference, serialized RunState, or a workflow checkpoint. Follow that mechanism’s recovery contract.
- Load the matching saved context. Restore the relevant session or run state, and rebuild the agent graph or workflow context required to interpret it. Do not silently substitute a different session or graph.
- Reconcile pending and completed work. Check which history writes or side effects are already committed. If the status is partial or ambiguous, stop or use a safe reconciliation path rather than assuming the action did not happen.
- Resolve any human decision. Associate the approval or rejection with the exact pending action before resuming. A decision for a different action is not a valid substitute.
- Continue using the chosen recovery operation. Resume the interrupted run when the saved execution is the thing being recovered. Use the selected conversation mechanism when starting a subsequent turn. These operations are not interchangeable.
- Record the outcome. Persist the resulting state and the status of external effects so another interruption can be handled from a known point.
For a concrete example, the OpenAI Agents SDK results documentation says to call to_state() to capture a resumable RunState, approve or reject pending items, and then resume with Runner.run(...) or Runner.run_streamed(...). The important distinction is that the serialized run state is what supports run resumption; merely supplying a conversation’s prior messages would be a different continuation strategy.
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Retries, reconciliation, and when to stop
Retry only when replay is safe
A transient failure does not prove that the request never reached its destination. If a provider received the request but the client did not receive the response, retrying can repeat provider-side work. The same uncertainty applies to a tool or external action whose response was lost. Before retrying, determine whether the operation is safe to replay, whether it has a reliable idempotency mechanism, or whether its result can be queried and reconciled. Do not label a retry “safe” merely because the original call returned an error.
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The OpenAI Python Agents SDK documents a specific recovery path for a failed session write: keep the same RunState and original Session backend and session ID. Before making another model call, the SDK checks whether the complete pending history batch is already present. If it is absent, the SDK retries the append; if the history is partial or ambiguous, it fails closed rather than continuing on uncertain context.
This behavior is specific to that documented SDK path, not a universal guarantee. An application using another framework or its own persistence layer needs an equivalent policy: establish whether the whole batch committed, retry only when the write is known to be absent, and stop if the stored history cannot be reconciled with confidence.
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Do not replay an unrecoverable terminal failure
The same Python SDK documentation marks certain failures to persist a terminal state as unrecoverable because replay could repeat terminal lifecycle effects. That is a framework-specific boundary. More broadly, if the system cannot tell whether a final effect or transition already occurred, continuing blindly can make the record less accurate. Preserve the failure for investigation or use a documented repair procedure rather than inventing a retry.
Human approval is a recovery boundary
A human-in-the-loop pause is a normal point at which an agent run can be saved and resumed later. The pending action and its approval status must remain connected across that pause. If the approval is lost, attached to the wrong action, or interpreted using a different workflow context, the resumed process may not be doing what the reviewer authorized.
The documented SDK flows support serializing a run while it waits, resolving pending approvals, and resuming later. OpenAI’s JavaScript guidance also describes restoring serialized state against a rebuilt agent graph. That makes graph compatibility part of the recovery problem: the restored state must be interpreted by the matching agents and workflow structure.
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Approval is not proof that an external action happened exactly once. The approval decision and the delivery of the resulting tool action are separate events. If the action might have succeeded before an interruption, reconcile its status before issuing it again; exactly-once behavior depends on the implementation’s delivery and side-effect controls, not on the approval step alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose a recovery design
Use these questions to compare the mechanisms available in a particular system:
- Who owns continuation state? Is it the application, an SDK session, a server-managed conversation, or the workflow runtime? Assign one clear owner for the normal path.
- What are you recovering? A later turn in an ongoing conversation, an interrupted run with pending work, or a workflow paused at a checkpoint require different mechanisms.
- What survives? Confirm whether the mechanism preserves only conversation history or also pending approvals, tool outputs, handoffs, staged inputs, and workflow progress.
- How are effects reconciled? Establish how the system detects a completed provider or tool action whose response may have been lost.
- What happens under ambiguity? Prefer an explicit stop or safe repair path over proceeding with partial history or an unknown side-effect status.
- What must be compatible? Check whether restoration requires the same session backend, framework release, or agent graph.
Workflow checkpoints are useful when work must be saved for later resumption. Microsoft Agent Framework documentation describes checkpoints for saving workflow state and resuming later. A checkpoint is a persistence mechanism, not evidence of universal exactly-once execution. The documented behavior can also change between releases, including checkpoint ordering, so verify details for the framework version actually deployed.
Common recovery mistakes
- Calling a new turn a resumed run: supplying prior conversation context may start a fresh model turn; it does not necessarily restart the paused execution or its pending tool loop.
- Mixing state owners: combining app-replayed history with SDK- or server-managed continuation without a clear boundary can duplicate or omit context.
- Assuming errors mean no side effect: a timeout or failed response can occur after a provider or tool has already acted.
- Restoring state with a different graph: serialized identities or workflow positions may no longer correspond to the agents rebuilt by the application.
- Continuing from partial history: the next model request may have a different view of the conversation than the run that was interrupted.
- Treating checkpointing as exactly-once delivery: saved workflow state does not, by itself, establish what happened in external systems.
To reduce these risks, test interruptions at the boundaries where state changes: before and after history writes, around tool calls, during approval waits, and during checkpoint persistence. For each case, verify whether a retry, resume, reconciliation, or stop is expected, and confirm that the saved state and external effects agree.
Version and implementation limits
Recovery behavior is framework- and release-specific. OpenAI’s Python and JavaScript Agents SDK documentation describes distinct APIs and recovery behavior; Microsoft Agent Framework documents its own workflow checkpoint model. Do not assume a field, retry guarantee, or serialization format transfers between them. Before deploying a recovery path, check the documentation for the exact SDK release, persistence backend, and workflow graph in use, and verify that saved state can be loaded after the upgrade procedure you plan to use.
There is no defensible general recovery-rate or interruption-frequency figure here: the official implementation documentation describes mechanisms, not a comparable population-level performance measurement. Evaluate a design with failure injection and application-specific records rather than treating a framework feature as a quantified reliability promise.
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