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Reliable AI agents are systems, not models acting alone. A model can request a tool call, but a runtime or hosted service must execute it, return the result, manage state, enforce limits, and decide whether the run is finished. To build an agent you can trust, design and test that whole loop—including what happens when it pauses, fails, or changes the environment.
What makes an AI agent more than a model?
An agent combines a model with a runtime, tools, conversation state, control rules, and an environment in which actions have effects. The model reasons from the information it receives and may emit a structured request to use a tool. That request is not the action itself: application code or a hosted service must run it and return the result.
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This distinction matters operationally. A model may request that a record be updated, but only the executing tool can attempt the update, report an error, or confirm the resulting state. The agent should not treat its own description of success as proof that the action occurred.
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What does the agent loop do?
A typical runtime repeats a controlled cycle until the model returns a final response, the run reaches a limit, or execution pauses or fails. The OpenAI Agents SDK guide calls the loop the core concept behind its SDK; the sequence below is an implementation example, not a universal API contract.
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- Prepare input: assemble the current task, relevant conversation state, tool definitions, and applicable control rules.
- Call the model: send the prepared input to the current agent and receive either a response, a structured tool request, or a handoff.
- Execute requested work: validate the request, check its permissions and arguments, then have the runtime or hosted service invoke the appropriate tool.
- Return the result: add the tool output to the state the model will see, including a clear error result if execution failed.
- Continue or stop: call the model again when continuation is allowed; transfer control on a supported handoff; otherwise return the final response or a clear paused or failed status.
Each iteration can change what the model knows and, when tools affect an external system, what is true in the environment. A later decision therefore depends on the correctness of earlier tool results and state updates.
Who should execute tools?
Execution can happen in application-controlled code or inside a hosted service. Anthropic’s tool-use documentation describes both client-executed tools, which require the application to complete the round trip, and server-executed tools, which can run a loop within the service. The choice determines which component must handle execution details and which traces or logs you can inspect.
| Approach | How the round trip works | Responsibility to examine |
|---|---|---|
| Application-executed tool | The model emits a tool request; the application validates and runs it, then returns the result to the conversation. | The application owns the integration path, including tool errors, access to external systems, approval checks, and the result supplied back to the model. |
| Hosted tool | A service executes the tool and may run the model-tool loop within its infrastructure. | Determine which controls, execution details, errors, and outputs the application can configure or observe; those details depend on the service’s documented contract. |
Neither approach is automatically more reliable. Compare them by the control the application needs, the execution responsibilities it can support, and the visibility it requires to investigate a run.
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How should a runtime manage state and stopping?
Continuation is a runtime decision, not an assumption that the model will eventually stop. Choose where state lives and how a subsequent model call resumes from it. The OpenAI Agents SDK guide describes three patterns: the application can hold and replay conversation history, an SDK session can manage it, or a server-managed conversation ID can identify it. These are different state-management choices; the runtime still needs a defined policy for what to resume.
Distinguish completion, pause, and failure
- Completed: the model has finished the requested work and no further tool work is pending. Return the final response and the run’s completion status.
- Paused: the run is intentionally waiting, for example, for approval. Preserve enough state to resume the same work after the decision; do not present the pause as a completed result.
- Failed: a tool error, validation problem, guardrail exception, or other runtime failure prevented normal continuation. Record the failure and make the next step explicit rather than silently treating the run as successful.
- Stopped by a limit: a maximum-turn or other configured cap was reached. Report that the run stopped at the limit, rather than implying the task is complete.
Set explicit continuation limits and specify what the caller receives when each stopping condition occurs. A limit is a safety control, not evidence that the task has been completed correctly.
Make approval a real state transition
If an action requires human approval, define which action is awaiting approval, what context the reviewer sees, and how an approved or rejected decision changes the run. The runtime should preserve the pending state and resume deliberately; an approval pause is not simply another ordinary model turn.
How can you see what happened during a run?
A final answer rarely explains why a run succeeded or failed. Use traces that let you follow the sequence of model calls, tool calls and outputs, handoffs, guardrails, and relevant workflow status. OpenAI’s tracing documentation describes recording these events, along with custom spans, while its trace-grading guidance covers using traces to identify workflow issues and regressions.
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Capture enough information to diagnose the work without logging secrets or unnecessary sensitive data. Decide which inputs, outputs, identifiers, and error details are safe to retain under your system’s privacy and security requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you evaluate agent reliability?
Test the task and its effects, not only whether the final answer sounds convincing. Anthropic’s evaluation guidance distinguishes an agent’s transcript from the final environment outcome: an agent can claim that an action succeeded even when the intended change did not occur. Multi-turn errors can also compound when one incorrect action changes the state used by later turns.
Build evaluations from observable task outcomes
- Define a task: specify the starting conditions, the requested work, and what counts as an acceptable result.
- Check the outcome: inspect the resulting environment state or another suitable task-specific signal, in addition to grading the conversation.
- Grade the trajectory: examine whether the agent used appropriate tools, respected required controls, recovered sensibly from errors, and stopped in the right state.
- Run multiple trials: model outputs can vary between attempts. Repeating tasks helps reveal behavior that a single successful run would miss.
- Keep comparisons repeatable: use a fixed set of tasks and consistent graders when comparing prompts or runtime changes over time.
OpenAI’s evaluation guidance describes moving from individual traces to datasets and repeatable evaluation runs once a team has criteria for good behavior. Traces help explain particular executions; a repeatable evaluation helps determine whether a change improved or regressed performance across tasks.
What should you compare when choosing an implementation?
There is no established universal winner across agent frameworks or execution models. Compare implementations against the needs of your application and the evidence you can collect.
| Decision axis | Questions to ask |
|---|---|
| Tool execution | Does application code or a hosted service execute tools? Who handles permissions, network access, errors, approvals, and returned results? |
| State and resumption | Is state held in application history, an SDK session, or a server-managed conversation? How does an intentionally paused run resume? |
| Observability | Can you inspect model calls, tool inputs and outputs, handoffs, guardrails, and workflow status in a trace? |
| Evaluation | Can you grade task outcomes and traces, repeat trials, and compare prompt or runtime changes against a stable set of tasks? |
| Control and operational burden | Which responsibilities remain with your application, which move to a hosted service, and is that division compatible with your approval and debugging requirements? |
A useful design review should be able to sketch the loop, identify its stop and resume paths, show how a failed tool call is represented, and explain how task outcomes are checked. If those behaviors cannot be observed or evaluated, a fluent final answer is not enough to establish reliability.
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