Coordinate an agent workflow by deciding separately who chooses the next step and what state survives between steps. Then define who owns that state, who can access it, and how a run resumes after a wait or failure. Those choices matter more than picking a single framework as a universal winner.
What state coordination means in an agent workflow
State coordination is the set of decisions that keeps a multi-step or multi-agent task coherent. It covers the workflow’s control flow, the information passed between steps, where that information lives, which workers can share it, and what happens when execution pauses or fails.
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These are related decisions, but they are not the same one. Orchestration determines which agent or step runs next. Persistence determines which information remains available across turns, process interruptions, or other boundaries. A system can have clear routing but weak recovery, or durable storage without a useful routing policy.
Choose who controls the next step
OpenAI’s Agents SDK documentation describes both model-directed and code-directed orchestration, and says, “You can mix and match these patterns.” The distinction is about control, not persistence.
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Model-directed orchestration
The model has more discretion to choose an agent or route based on the task. This can fit open-ended work where the next useful step depends on interpreting the request or results. It also means routing decisions need to be made observable and tested; do not treat model discretion as a substitute for enforcing fixed business or safety rules.
Code-directed orchestration
The application defines the workflow and decides which step runs next. This is a natural fit when the sequence or routing conditions are fixed and need to remain inspectable. The trade-off is that the application must encode and maintain that flow.
Mixed control
A mixed policy can reserve required transitions and guardrails for application code while allowing model-directed choices within permitted parts of the workflow. This is an implementation recommendation based on the documented distinction, not a performance claim or a vendor benchmark.
Choose the state owner and boundary
Before selecting storage, decide what one state object represents. It might belong to a user conversation, a workflow run, an agent handoff, or durable business data. Those lifecycles are not interchangeable: a conversation can contain multiple runs, while a run can involve several agents and handoffs.
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Keep identifiers and lifetimes clear. As a design practice, give independently executing runs distinct identities and avoid letting concurrent work mutate the same unpartitioned state. The documentation establishes different session, conversation, and response-continuation resources; it does not prescribe a universal state schema.
OpenAI’s Agents SDK documentation distinguishes application-managed history, SDK sessions, and OpenAI-managed continuation options. A conversation object, an SDK session, and a sandbox are different resources; do not assume one is equivalent to another. The OpenAI API documentation ties its managed conversation and response-continuation options to the Responses API.
Compare persistence options by ownership and recovery
The OpenAI Agents SDK documentation recommends choosing one persistence strategy per conversation. Combining layers may be justified, but only where each layer has a defined responsibility—for example, a distinct business record alongside conversation state—rather than simply duplicating history.
| Pattern | State ownership | Storage or scope | Recovery implications |
|---|---|---|---|
| Application-managed history | The application controls the history and its storage. | Application-selected; exact backing store is not specified in the cited SDK guidance. | The application is responsible for the persistence and continuation behavior it needs. |
| Agents SDK session | The application uses the SDK session abstraction and its chosen backing option. | Documented options include SQLite, Redis, a Dapr state store, and OpenAI-hosted storage. | Session persistence stores state; durable resumption across long waits or process restarts depends on the broader workflow design. |
| OpenAI-managed conversation or response continuation | Platform-managed through the Responses API. | Separate API continuation resources; do not treat a conversation and a response continuation as the same resource. | Provides API-level continuation options; durable workflow behavior across arbitrary waits, retries, or restarts is not established by this distinction alone. |
| LangGraph persistence and durable execution | Framework-level workflow state. | LangGraph is documented as a low-level framework for stateful, long-running workflows. | Its reference documents persistence and durable execution capabilities; this is not an apples-to-apples performance comparison with SDK sessions. |
For SDK sessions, select a backing option based on the deployment’s sharing and operational needs. SQLite, Redis, Dapr state store, and OpenAI-hosted storage are documented options, not a ranking. Confirm the current constraints and behavior of the selected option in the relevant documentation before relying on it in production.
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Design for waits, retries, and restarts
A persisted conversation is not automatically a resumable workflow. If a run can wait for human approval, an external system, or a long-running operation—or must survive a process restart—identify the workflow layer that records progress and determines what can safely run again.
The OpenAI Agents SDK integration guide names Dapr, Temporal, and Restate for durable-execution use cases. LangGraph documents durable execution and persistence as capabilities. These references establish available approaches, not a common comparison of reliability, latency, or feature parity. Check the integration’s current status and capabilities in its own documentation before implementation.
For any recovery design, decide which steps can be repeated and how you prevent a retry from duplicating an external side effect. Treat that as an application-level requirement: the cited documentation does not establish a universal retry or idempotency policy across these options.
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Use this decision framework
- Fix the control policy. Mark which transitions are mandatory and must be selected by application code. Decide where model-directed routing is acceptable, and whether a mixed policy best fits the workflow.
- Name the state boundary. Decide whether each piece of data belongs to a conversation, run, handoff, or durable business record. Assign identities and lifetimes that keep concurrent work separate.
- Select the persistence owner. Choose application-managed history, an SDK session, or a Responses API managed continuation path according to who should own and operate the data.
- Check sharing and runtime constraints. Determine whether workers or services need common access, then verify the selected store and API fit the deployment. The SDK documents SQLite, Redis, Dapr state store, and OpenAI-hosted storage as session options.
- Set the recovery requirement. If simple continuation is enough, use a persistence approach that serves that need. If the workflow must resume across waits, retries, or restarts, assess a durable-execution integration or framework and verify its current behavior.
- Instrument transitions. Record state transitions, agent handoffs, retries, and persistence failures in the chosen runtime so that an incomplete or unexpected run can be diagnosed.
What the documentation does—and does not—establish
The OpenAI Agents SDK and API documentation and the LangGraph reference retrieved on October 7, 2026 describe capabilities and resource distinctions, not comparative benchmarks. They do not establish which option is fastest, most reliable, cheapest, or best for every application. No common reliability or latency measurements are provided here, so make those judgments with evidence from your own requirements and deployment rather than assuming a framework-wide advantage.
Documentation can change. Confirm the current API, SDK, framework, and integration behavior for the versions you deploy.
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