To manage state in an AI agent, first decide what must persist, who should own it, and how it will be recovered. An active run’s execution state, conversation history reused on later turns, and durable application data or learned memory are different things. Treating them as one feature can create duplicated context, weak authorization, or data-retention surprises.
For an OpenAI-based implementation, the documented choices include managing history yourself with the Responses API, using a persistent session with the Agents SDK, or using the managed Agents API. Pick the approach that fits your control, sharing, recovery, context-growth, and data-governance requirements. The official material describes implementation options, not a measured winner.
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What does state mean in an AI agent?
State is information that affects what an agent does now or later. Before choosing storage, separate three categories that are often grouped under “memory.”
Execution state for the active run
This is the information needed to carry out work in progress: current steps, intermediate results, and the point the agent has reached. It may be short-lived, but if work can pause for approval, wait for an external event, or need to resume after a process restart, it needs a deliberate recovery plan.
Conversation history for later turns
This is the prior interaction or run information supplied or retrieved when the agent handles another turn. A session can provide this continuity by retrieving earlier items before a run and storing new items afterward. History is not necessarily the same as a durable record of what the business knows or what a user has asked the application to remember.
Durable application data or learned memory
This includes records that should remain authoritative beyond one conversation, such as an account preference, an order, or an approval status. Keep such business records in application-owned systems with explicit access, retention, and deletion rules. A conversation transcript or session store should not silently become the source of truth for them.
How do I manage state in an AI agent?
Choose the state owner and continuation mechanism based on the application’s requirements. OpenAI’s documentation describes three broad approaches: a managed Agents API, an application-run Agents SDK, and direct use of the Responses API. The exact state arrangement differs by approach.
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| Approach | Where continuity is managed | Best fit to evaluate | Trade-off to account for |
|---|---|---|---|
| Responses API with application-managed history | Your application stores and supplies the history it wants to carry forward; a small loop can pass forward the previous result’s input list. | Applications that need direct control over which prior items enter the next model call. | Your application must implement persistence, retrieval, filtering, and any recovery behavior it needs. |
| Responses API with server-managed continuation | Use the corresponding conversation ID or prior-response continuation mechanism documented for the Responses API. | Applications that prefer provider-managed conversation continuation over rebuilding all history themselves. | Check the product’s current state and data terms; avoid layering a second history strategy on top without reconciliation. |
| Agents SDK with a session | An SDK session retrieves prior items before a run and stores new run items afterward, using a chosen backing store. | SDK applications that need persistent conversation continuity across runs. | The application still chooses and operates or configures the storage layer, and must decide what history remains useful. |
| Managed Agents API | State is managed across turns by the hosted Agents API. | Applications that want a provider-managed runtime and whose data and deployment constraints fit the current terms. | The current Agents API documentation states US-only data residency and no Zero Data Retention support; verify current terms before adopting it. |
This comparison is about ownership and documented mechanics, not speed, reliability, cost, or overall quality. The available official material does not establish a comparative benchmark.
How can an AI agent remember context between runs?
Use one persistence strategy for a conversation unless you deliberately reconcile the layers. The Agents SDK guide, “Running agents,” warns that combining client-managed history with server-managed continuation can duplicate context. It advises: “In most applications, pick one persistence strategy per conversation.”
Pass history forward yourself
For a small loop where application control matters, store the relevant prior input list and pass it into the next run. This makes the application responsible for deciding what to retain and provide, including how it handles history growth.
Attach a persistent SDK session
For an SDK-backed conversation, attach a session that uses the same backing store on subsequent runs. The SDK session documentation describes retrieving prior items before a run and saving new run items afterward. Its examples include SQLite, Redis, and hosted storage; these are implementation choices, not a ranking.
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Use provider-managed continuation
For the Responses API’s server-managed continuation options, continue using the corresponding conversation ID or prior response mechanism. Do not also feed a full client-managed copy of that conversation into the same run unless the application intentionally reconciles which items belong in each layer.
How should you choose a persistence approach?
Start with the operational need rather than the word “memory.” These questions expose the design decision without implying that one option is best for every agent.
- Who must control the record? If the application needs to decide exactly which history items reach the model, application-managed storage and history selection provide that control. If provider-managed continuation is acceptable, assess its behavior and terms directly.
- Who needs to share it? Decide whether state is only used by one process or session, or must be accessible across workers or services. Confirm that the selected store and deployment make that possible.
- What must survive? Ordinary turn continuity differs from a workflow that may wait for approval, retry, or resume after a restart. For extended work, assess durable orchestration rather than assuming conversation history alone is a recovery mechanism.
- How will context grow? Decide what to retrieve, filter, limit, or summarize before another model call. SDK documentation shows retaining recent history and setting session limits as possible mechanisms; it does not establish one optimal truncation policy.
- What data may be persisted? Identify where state resides, who can access it, how retention and deletion work, and whether secrets could enter serialized context or history.
- What operations will you own? Application-managed persistence adds work for storage, retrieval, filtering, and recovery. A hosted or orchestration service changes that responsibility, but still requires checking its behavior, limits, and data terms.
How should an agent handle context, secrets, and authorization?
Keep application context and authorization logic under application control. The Agents SDK’s “Context management” guide states: “The context object is not sent to the LLM.” Treat that as a property of the documented SDK context object, not as a blanket guarantee about every kind of stored or transmitted state.
Do not treat a model-selected tool argument as permission. A tool being available to the agent does not itself authorize access to a particular resource. Enforce permission checks in the tool implementation or another application authorization layer, and keep secrets out of context that may be serialized or transmitted.
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What changes when work must survive a pause or failure?
A session can preserve conversation items, but long-running work may also need durable orchestration: for example, when an agent waits for human approval, retries after a failure, or must recover progress after a process restart. The Agents SDK guide names integrations including Dapr, Temporal, Restate, and DBOS for long-running workflows, approvals, and progress recovery.
The documentation does not provide a comparative benchmark among these integrations. Before selecting one, verify its current persistence semantics, failure and retry behavior, limitations, and operational requirements against the workflow you need to support.
What data controls should you check for a hosted runtime?
Check geography, retention, deletion, and data-residency terms before choosing a hosted state service. As documented on 2026-10-05, the OpenAI Agents API documentation states that state is retained across turns, that data residency is US-only, and that Zero Data Retention is not supported. Those statements apply to the Agents API documentation cited here; do not generalize them to the Agents SDK, other OpenAI products, or other vendors. Product terms can change, so confirm the current documentation and applicable terms for your deployment before storing sensitive data.
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