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A production AI agent is a control loop, not a single prompt followed by a single answer. It calls a model, acts on requested tools, may hand work to another agent or pause for approval, and continues until it can return a final result—or must stop in a recoverable failure state. To make that workflow reliable, define who owns its state, what each transition does, how it resumes, and what operators can observe.
What does an agent state machine need to represent?
A useful state machine makes the workflow’s control decisions explicit. The labels below are a design aid, not required SDK enum values. A small implementation may represent them as code branches; a more complex one may need a persisted workflow graph.
| State | Meaning | Typical next transition |
|---|---|---|
ready |
The workflow has input and can begin or continue a turn. | model_call |
model_call |
The current agent is being called with the context it owns. | tool_pending, handoff, completed, or failed |
tool_pending |
The model has requested a tool action, but execution has not begun. | tool_running or awaiting_approval |
tool_running |
The application is carrying out an approved tool call. | model_call or failed |
handoff |
Control is being transferred to a specialist agent. | model_call under the receiving agent |
awaiting_approval |
A human decision is required before a pending action can proceed. | resumable after approval or rejection is recorded |
resumable |
The workflow has saved enough state to continue after a pause. | The next permitted action, often tool_running or model_call |
completed |
The workflow has a final answer and no more tool work to perform. | Terminal |
failed |
The workflow cannot continue automatically under its current retry policy. | Terminal or an explicit recovery path |
For every transition, document four things: its trigger, the state that must be saved, the side effect it performs, and the condition for retry or completion. This prevents ambiguous situations such as a tool having run while the workflow still believes it is pending, or a process restarting without knowing whether approval was already given.
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How does the agent control loop proceed?
One SDK run is an application-level turn. The application calls the current agent’s model, examines the result, executes requested tools and continues the loop. If control is handed to another agent, the receiving agent continues on that branch. The run returns when the agent produces a final answer without more tool work. Treat that as the runtime loop; do not assume one model call equals one completed user request.
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- Enter a turn. Load the conversation or workflow state using the chosen state-ownership strategy, then call the current agent’s model.
- Inspect the output. If it requests a tool, record the pending action and check whether it can run immediately or requires approval.
- Run permitted tools. Execute the tool action, record its outcome, and continue with the appropriate agent and updated context.
- Handle a handoff. Change the active agent when the workflow transfers control to a specialist, while preserving the state needed by the receiving branch.
- Stop deliberately. Return a final answer only when no further tool work is requested. If the workflow is interrupted or cannot proceed, persist a resumable or failed state instead of presenting an incomplete run as complete.
The application should treat model output as an input to this control logic, not as proof that an external action succeeded. The workflow—not the wording of a generated answer—must record whether a tool was requested, whether it ran, and what result it returned.
Who owns conversation state and continuation?
State can live in application-managed replay history, a persisted SDK session, a server-managed conversation ID, or a previous response ID. These are alternative continuation strategies, not interchangeable labels for the same storage. In most cases, choose one strategy for a conversation and make its owner explicit.
- Application-owned replay history: The application retains and supplies the conversation history it needs on each turn. This gives the application direct control over what is replayed, but it must persist and manage that history.
- Persisted session: A session mechanism stores conversation state for continuation. The application must still decide how session identity and persistence fit the workflow’s lifecycle.
- Server-managed conversation ID: The application continues against a conversation identified by an ID, with state managed on the server side.
- Previous response ID: The application refers to an earlier response when continuing, rather than rebuilding the entire continuation context itself.
A conversation or session identifier can preserve context between turns. That alone does not make a workflow durable across long waits or process restarts: conversational context and workflow execution state solve different problems. If the application combines local replay with server-managed state, it should reconcile which messages are already represented; otherwise, it can duplicate context.
When should a specialist take over, and when should it be a tool?
The central question is who owns the final user-facing answer at each branch. A handoff and an agent used as a tool assign that responsibility differently.
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| Pattern | Control and answer ownership | Use it when |
|---|---|---|
| Handoff | A specialist takes over the conversation branch. | The workflow intentionally transfers execution to an agent with a distinct responsibility. |
| Agent as a tool | The manager calls a specialist, remains in control, and is responsible for the final answer. | The manager needs specialist work as an input to its own response. |
Delegation is an architectural choice, not a goal in itself. Add a specialist when it materially improves capability, policy isolation, prompt clarity, or trace legibility. Keep its scope narrow enough that the owning agent’s responsibilities and the transfer of control remain clear.
How should approval pauses and resumption work?
An approval pause is an incomplete workflow state, not a completed answer. An interrupted run may have no final output because a decision is still pending. The interruption data identifies pending tool calls, and the saved state can be passed back after approval or rejection.
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- Record the pending action. Save enough information to identify the requested tool call and the workflow branch that produced it.
- Persist the interruption state. Keep the resumable snapshot and the pending approval details together so the application can determine what is waiting.
- Record the human decision. Store whether the action was approved or rejected before continuing.
- Resume from the saved state. Pass the saved state back into the workflow and allow only the action permitted by the recorded decision.
- Continue the control loop. After the approved action runs—or the rejected action is handled—the workflow can return to the model loop or stop according to its design.
Do not treat the existence of a snapshot as permission to repeat a side effect. The application should define whether an interrupted tool action had started, whether it completed, and what evidence is needed before retrying. The correct recovery path depends on the tool and the workflow’s side effects.
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A compact tool loop can live in ordinary application code. Make the workflow graph and persistence boundary explicit when branching, long waits, human review, retries, or process restarts become central requirements. An SDK session or conversation ID helps with context continuation; it is not by itself a guarantee that the whole workflow will survive a worker or process disappearing.
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The OpenAI Agents SDK guide names Dapr, Temporal, and Restate integrations for durable or long-running use cases. The documentation does not establish a universal winner or provide a comparative benchmark, so choose based on the execution guarantees and operating model the application needs rather than assuming one integration is best for every agent.
- Identify which workflow state must survive a restart, not just which messages must remain in context.
- Define how the system records pending work, approval decisions, tool outcomes, and retry eligibility.
- Decide what happens when a worker stops during a side effect, including how the application avoids unsafe duplicate execution.
- Use an orchestration layer when its persistence and recovery model addresses requirements the application would otherwise have to build and operate itself.
What should operators be able to see?
State durability and observability are separate needs. Tracing can record model calls, tool executions, handoffs, and guardrails, helping operators understand how a run reached its current state. A trace does not itself make a workflow durable; persistence and recovery still need to be designed.
Data policy affects tracing availability: OpenAI’s Agents SDK documentation states that tracing is unavailable for organizations using OpenAI’s APIs under a Zero Data Retention policy. Confirm that the selected tracing approach is compatible with the deployment’s retention requirements before relying on it for operational visibility.
At minimum, make it possible to answer these questions for a run:
- Which agent currently owns the branch, and who is responsible for the final answer?
- What was the last completed transition, and what transition is pending?
- Which tool calls ran, which are awaiting approval, and what outcomes were recorded?
- Can this run resume from saved state, and what condition permits a retry?
- What model calls, handoffs, tools, or guardrail events are available to inspect under the deployment’s data policy?
How to choose a state-machine design
Start with the simplest design that satisfies the workflow’s actual failure and continuation requirements. A short interaction that runs synchronously may need only a clear application-level loop and one chosen context strategy. A workflow that waits for a person, retries external work, or must recover after a restart needs explicit persisted workflow state and a defined resumption path.
Quick Recap
- Assign ownership. Name the component responsible for conversation context, workflow progress, pending side effects, and the final user-facing answer.
- Define transitions. Specify the trigger, saved state, side effect, and completion or retry condition for each meaningful edge.
- Separate pause from completion. Represent approval waits and interruptions as resumable states, not as successful final answers.
- Choose persistence for the failure model. Decide whether application-level state is sufficient or whether long waits and restarts call for a durable orchestration integration.
- Plan visibility with data policy. Decide which events operators need to inspect and verify the tracing option can be used under the organization’s retention rules.
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