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Reliable AI agents need more than a well-configured model: their runtime must define when a run ends, who owns conversation state, where validation occurs, how work changes hands, and how the workflow is observed and tested. OpenAI’s Agents SDK documentation provides concrete examples of these choices; details such as guardrail boundaries and continuation methods are specific to that system and should not be assumed to work identically in every framework.
1. Define the run loop and its stopping conditions
An agent run is an application-level turn, not necessarily one model call. In the OpenAI Agents SDK, the runner calls the current agent’s model, examines the result, executes requested tool calls or transfers control through a handoff, and continues until it reaches a final answer with no further tool work. As OpenAI’s Running agents documentation puts it: “The runner keeps looping until it reaches a real stopping point.”
Make that stopping point explicit in your application. A run should be considered complete only when the workflow has reached its defined final state—not merely because one model response arrived. Separate successful completion from runtime errors and validation failures so callers can respond appropriately instead of treating every ended run as success.
Handle approval pauses as pauses
Some workflows need to wait for a person to approve an action. That is an expected pause, not a failure: preserve the run’s state and resume it after approval rather than restarting from scratch or reporting it as complete. Define what information must be saved, who can approve, and what happens if approval is denied or never arrives.
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2. Choose deliberately who owns conversation state
Continuation design decides which component is responsible for remembering prior turns and what the application must supply to continue. OpenAI’s documented approaches include passing history managed by the application, using a storage-backed session, continuing with a server-managed conversation ID, or providing a previous response ID.
| Strategy | State ownership and continuation | Key trade-off |
|---|---|---|
| Application-managed input history | The application keeps the conversation history and supplies it as input on subsequent turns. | Gives the application direct control, while requiring it to manage and pass the relevant history. |
| Storage-backed session | A session persists conversation state using storage. | Provides a session-based continuation path; storage and session behavior must be accounted for in the application design. |
| Server-managed conversation ID | The application continues against a conversation identified on the server. | Reduces the history the application needs to resubmit, but ties continuation to the relevant API. |
| Previous response ID | The application supplies the ID of a prior response to continue from. | Uses provider-specific continuation rather than requiring the application to resend the entire history. |
Do not combine client-managed history with server-managed continuation casually. If both contain the same prior turns and the application does not reconcile them, context can be duplicated. Choose one clear source of truth, document what gets passed on each turn, and ensure paused work can be resumed with the state that strategy requires.
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3. Validate at the boundaries where risk enters or leaves
“Guardrails” are not one universal check. Place checks around the specific boundary they are intended to protect, and verify how the chosen framework invokes them. The OpenAI JavaScript Agents SDK documents three distinct scopes:
- Input checks: inspect incoming content. In the documented SDK behavior, input guardrails run only for the first agent in a chain.
- Tool checks: inspect custom function-tool calls. The SDK runs tool guardrails around each custom function tool; do not assume that this describes every tool type or another framework’s behavior.
- Output checks: inspect a response before it is delivered as the final answer. In the documented SDK behavior, output guardrails run only for the final agent in a chain.
These boundaries matter in a multi-agent workflow: a check attached to the first agent is not automatically a check at every later handoff, and a final-answer check is not a substitute for controlling tool actions. Map each risk to the relevant input, tool, or output boundary, then confirm whether the check blocks work or runs alongside it in your implementation.
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4. Make handoffs explicit and purposeful
A handoff transfers work from one agent to another, often to a specialist. Treat it as a workflow transition with an accountable owner, not as an implicit signal that more agents will necessarily improve the result. OpenAI’s orchestration guidance frames the ownership pattern as a design choice; it does not establish that multi-agent structures automatically improve quality or lower cost.
For each agent, specify:
- Role: the task it owns and the point at which it should hand work onward.
- Tools: the tools it may use, limited to what its role requires.
- Output contract: the result and context the next step needs, including how uncertainty or failure is represented.
- Ownership transition: which agent or application component is responsible after the handoff.
These contracts make it easier to inspect a run when work changes hands and to decide whether a handoff was appropriate.
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5. Trace runs, while treating trace content as sensitive
A trace records workflow activity across a run, rather than showing only its final answer. Depending on configuration, tracing can expose model responses, tool calls, guardrails, handoffs, inputs and outputs, duration, and status. OpenAI’s Evaluate agent workflows documentation describes a trace as “the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run.” That view can help locate where behavior went wrong, such as an unexpected tool call or a misrouted handoff.
Trace data can also contain sensitive information. Before enabling trace export or sharing traces with other systems or people:
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- Check which inputs and outputs the trace configuration includes or excludes.
- Confirm that the data handling and retention meet your organization’s requirements.
- Limit access to traces according to the sensitivity of their contents.
OpenAI’s Agents SDK documentation says tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy. Check current platform terms and configuration for your organization before relying on tracing.
6. Evaluate the workflow, not only the final prose
A fluent final answer does not show whether the agent chose the right tool, followed the intended route, or respected a policy along the way. OpenAI’s agent-evaluation guidance describes using traces, graders, datasets, and evaluation runs to examine behavior across the workflow. Trace grading can help assess tool selection, handoff decisions, instruction adherence, and safety-policy behavior.
Keep representative cases for the tasks and edge cases your workflow is expected to handle. Rerun them when prompts, tools, routing, or other workflow behavior changes, and examine traces when a result regresses. Evaluation is evidence about the cases and checks you have run; no single setup proves that a workflow is safe or correct in every situation.
7. Match deployment and orchestration to operational needs
Where orchestration runs determines who manages state, approvals, persistence, and the surrounding runtime. OpenAI’s overview describes the SDK as letting the application control deployment, storage, approvals, and runtime integration. Its SDK guide also points to durable orchestration integrations for workflows that need to survive long waits, retries, or process restarts. These are options to assess against operational needs, not a universal framework ranking.
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| Operational need | What to decide |
|---|---|
| Runtime and storage control | Decide how much control the application needs over deployment and persistence, and which component will own stored state. |
| Human approval | Define how a run pauses for approval, how its state is saved, and how it resumes after a decision. |
| Long waits, retries, or restarts | Assess whether the workflow needs durable orchestration so it can continue across delays or process interruptions. |
| Operational complexity | Weigh the control and durability you need against the extra integration and operational responsibility they bring. |
For a short-lived workflow, application-level orchestration may be sufficient. If a process must reliably resume after a long wait, retry, or restart, evaluate a durable orchestration integration and account for its state and approval model.
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