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Building an agent is only part of the job. At runtime, an application must decide when a run is finished, where its conversation state lives, which checks apply at each boundary, how work moves between agents, and what to do when a run pauses or fails. These seven practices use OpenAI’s Agents SDK documentation as a concrete example; details such as guardrail behavior are framework-specific and should not be assumed to apply elsewhere.
1. Define the run loop and its stopping conditions
An agent run is a sequence of model calls and application actions, not necessarily one prompt followed by one answer. In OpenAI’s Agents SDK, the runner calls the current agent’s model, examines the result, executes tool calls or transfers control through a handoff, and continues until it receives a final answer with no further tool work. OpenAI’s Running agents guide summarizes the loop: “The runner keeps looping until it reaches a real stopping point.”
Make that stopping point explicit in your application. A run should end as a normal completion only when the workflow has reached its intended outcome and any required validation has passed. Tool errors, timeouts, malformed results, and failed validation need distinct failure handling rather than being treated as successful answers. The exact error types and limits depend on the runtime you use.
Distinguish completion, failure, and pause
- Completion: the agent has returned a final result and no more tool work is pending.
- Failure: a runtime or validation problem prevents the workflow from completing as intended. Surface or record the failure rather than silently presenting an unverified result as successful.
- Pause: the workflow is waiting for an expected event, such as human approval. In the documented SDK pattern, preserve the paused run’s state and resume it when the approval step is resolved; do not classify the pause itself as a failure.
This distinction is operationally important: a caller needs to know whether to display a result, report an error, or wait and resume later.
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Continuation design determines what your application must persist and send on the next turn. OpenAI’s documented options include application-managed input history, a session backed by storage, a server-managed conversation ID, and a previous response ID. They are different state-ownership choices, not interchangeable labels for the same mechanism.
| Continuation approach | Who owns persistence? | What is passed or retained? | Main consideration |
|---|---|---|---|
| Application-managed input history | Your application | The application supplies the relevant history on subsequent turns. | Gives your code direct control over what context is retained and resubmitted. |
| Session backed by storage | A session layer backed by storage | The session carries conversation continuity; the storage and lifecycle details depend on the implementation. | Clarify how the session is persisted and how paused work is recovered. |
| Server-managed conversation ID | The provider’s conversation service | A conversation identifier is used to continue the relevant server-side conversation. | Reduces the need for the application to resubmit history, but ties continuation to that API. |
| Previous response ID | Provider-managed response continuation | A prior response ID is supplied to continue the associated interaction. | Also provider-specific; confirm how it fits your retention and recovery requirements. |
The table describes the choices documented by OpenAI, not a feature comparison across providers. Check the current API and SDK documentation for the persistence details of the option you select. In particular, avoid maintaining a full client-side history while also continuing server-side state unless you reconcile the two: combining both without a clear rule can duplicate context.
Make recovery part of state design
Decide what happens to state when a run pauses for approval or the process handling it stops. Record enough continuation information to resume the intended work, and define which component is authoritative if state is represented in more than one place. A session or identifier is useful only if your application can reliably associate it with the right user, workflow, and pending action.
3. Validate at the boundaries that matter
Guardrails are not one universal check. Separate the content entering the workflow, the actions taken through tools, and the answer leaving the workflow. This makes it possible to reason about what each check can prevent and where it must run.
| Boundary | What it checks | OpenAI JavaScript SDK behavior documented in the guide |
|---|---|---|
| Input | Incoming content before the workflow proceeds. | Input guardrails run only for the first agent in a chain. |
| Tool | Custom function-tool calls and their execution boundary. | Tool guardrails run around each custom function tool. |
| Output | The final answer before it is delivered. | Output guardrails run only for the final agent. |
These are framework-specific boundary semantics from OpenAI’s JavaScript SDK documentation. Verify the behavior of the SDK and version you use, including which tool types are covered; do not assume a check around custom function tools also applies to every built-in or external tool. Also decide whether a check blocks work or runs alongside it, and what the application does when it rejects a result.
Test the boundary, not just the checker
- Confirm that the input check runs where incoming content first enters the chain.
- Exercise each custom function tool to establish that the intended tool check actually runs around it.
- Verify that the final output check runs before delivery, including after a handoff.
- Test rejection paths so blocked inputs, tool calls, or outputs cannot accidentally be treated as successful completions.
4. Keep handoffs explicit and purposeful
A handoff transfers work from one agent to another, often because a specialist is better suited to a particular task. It adds a control-flow transition: the application needs to know which agent now owns the work and what information or result should pass between them.
For each agent, define a narrow role, the tools it may use, and the output contract expected by the next step. Make the routing decision and current owner easy to inspect, especially when a workflow has multiple handoffs. OpenAI’s orchestration guide treats the ownership pattern as a design choice; it does not establish that adding agents automatically improves quality or lowers cost.
Specify the handoff contract
- State what work the receiving agent is responsible for.
- Define the information it needs and the form of the result it should return.
- Make clear which agent or application component handles the next decision.
- Keep each agent’s tool access aligned with its role.
If a task does not benefit from a distinct specialist or a clear ownership transfer, an extra handoff may add complexity without solving a real problem.
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A final answer shows what the agent said, but not necessarily how it got there. A trace can record the steps in a run, including model responses, tool calls, guardrails, and handoffs. OpenAI describes a trace as an end-to-end record for one run; tracing views can also expose information such as inputs, outputs, duration, and status.
Use traces to investigate workflow behavior across steps: for example, whether the agent chose an unexpected tool, handed off at the wrong point, or returned a result after a problematic intermediate action. A trace is most useful when the team can connect the recorded steps to the outcome and the relevant run context.
Set data-handling rules before enabling export
Trace configuration can include or exclude potentially sensitive inputs and outputs. Decide what may be recorded, who may access it, and how it fits your organization’s retention requirements before enabling trace export. OpenAI’s Agents SDK documentation states that tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy. Confirm current provider terms and configuration for your organization rather than assuming traces are available or appropriate by default.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Evaluate the whole workflow, not just its prose
A fluent final answer does not prove that the agent used the right tool, followed the intended route, or respected a policy at each stage. OpenAI’s agent-evaluation guide describes using traces, graders, datasets, and evaluation runs. Trace grading can help investigate tool selection, handoff decisions, instruction adherence, and safety-policy behavior, as well as changes in end-to-end behavior after a prompt or routing edit.
Build a repeatable evaluation loop
- Keep representative cases. Include ordinary requests and cases that exercise important tools, handoffs, approval pauses, and rejection paths.
- Inspect traces for workflow behavior. Look beyond the final response to see which steps led to it.
- Grade the properties that matter. Evaluate tool choice, routing, instruction adherence, and policy behavior alongside answer quality.
- Rerun cases after changes. Compare behavior when prompts, tools, or routing logic change so that regressions become visible.
Evaluation results are evidence about the cases and criteria you tested, not proof that a workflow is safe or correct in every situation. Keep the criteria aligned with the risks and responsibilities of the application.
7. Match deployment and orchestration to operational needs
Runtime architecture affects where orchestration happens, who manages state, and how the workflow handles approvals and interruptions. OpenAI’s overview presents its SDK as a way for applications to control deployment, storage, approvals, and runtime integration. Its SDK guide also points to durable orchestration integrations for workflows that span long waits, retries, or process restarts.
| Operational need | What to compare | Trade-off to make explicit |
|---|---|---|
| Control of runtime and storage | Whether the application controls deployment and persistence or delegates more of that responsibility. | More direct control means the application team owns more implementation and operational choices. |
| Human approvals | How an approval pause is represented, stored, and resumed. | A workflow that waits for people needs a reliable path to preserve pending work and continue it later. |
| Long waits, retries, or restarts | Whether the orchestration approach can carry work across these interruptions. | Durable orchestration may fit better when work outlives a single process, but it brings its own integration and operational complexity. |
| State ownership | Which component is authoritative for conversation and workflow state. | Clear ownership simplifies continuation and recovery; split ownership requires reconciliation. |
There is no universal framework ranking established here. Choose based on the actual lifecycle of the workflow: a short, request-bound interaction has different operational needs from a process that waits for approval or must survive a restart. Document who owns state, how a pause resumes, and what happens when retries or failures occur before deploying the workflow.
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