Reliable AI agents are systems built around a model, a runtime, and tools—not models acting alone. The model can request an operation; application code or a hosted service executes it and returns the result. Reliability comes from controlling how that loop continues and stops, handling errors and approvals deliberately, inspecting what happened, and evaluating whether the task actually succeeded.
What happens in an agent loop?
A tool call is a structured request from the model, not an action the model performs itself. In a client-executed setup, the application receives the request, runs the operation, sends the result back into the conversation, and asks the model to continue. The model may then request another tool or produce a final answer.
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A typical runtime follows this sequence:
- Prepare input. The runtime assembles the current conversation state and calls the model.
- Inspect the response. If it contains tool calls, the runtime validates and routes them; if it contains a handoff, the runtime transfers control to the designated agent or specialist.
- Execute and return results. The runtime or a hosted service runs permitted tools, then supplies their outputs to the conversation.
- Continue or stop. The runtime calls the model again when work remains, and returns a final answer when no further tool work is requested—or stops for an error, limit, or approval pause.
The OpenAI Agents SDK guide describes this pattern as the core of its SDK: “That loop is the core concept behind the SDK.” It is a useful implementation example, not a universal contract for every agent framework.
For example, a support agent asked to check an order might request an order-lookup tool. The application—not the model—checks whether that request is allowed, runs the lookup, and returns the result. The model can then explain the status or request another permitted operation. The answer should not be treated as proof that the lookup or any follow-up action succeeded; the runtime needs to verify the result.
Who executes tools, and who owns the consequences?
With client-executed tools, the application controls the operation and the round trip. That makes the application responsible for such concerns as validating requests, managing credentials and network access, deciding whether approval is required, handling tool errors, and returning an appropriate result to the model.
With server-executed tools, a hosted service can run the tool and, in some cases, manage the loop within its infrastructure. This can shift operational work away from the application, but it also changes which system runs the operation and what the application can directly control or observe. The specific responsibilities depend on the service and tool; do not assume that “hosted” means an action is automatically safe or that the application has no remaining obligations.
When choosing an approach, establish which component authorizes each operation, where errors are surfaced, how results reach the model, and what information is available to diagnose a run. These responsibilities matter more than a generic claim that one execution mode is always better.
How should state, stopping, and resumption work?
A multi-step run needs an explicit state strategy: the runtime must know what has happened so far and what context to use on the next model call. The OpenAI Agents SDK guide describes three patterns: the application can retain and replay conversation history, an SDK session can manage it, or a server-managed conversation ID can carry it forward. The right choice depends on the application’s control and persistence needs; the guide does not make one a universal default.
Stopping is part of the design, not an afterthought. A run can end normally when no more tool work is needed, stop after a configured turn limit, fail because of a tool or runtime error, or pause because a human approval is required. Represent these outcomes distinctly. In particular, a paused run is not a completed run: preserve enough state to resume it after approval, and make the transition back into execution explicit.
- Normal completion: return the final answer and record that the run completed.
- Approval pause: preserve the pending action and relevant state; resume only after the required decision.
- Failure: identify whether the cause was a tool error, validation problem, guardrail exception, or runtime issue, and apply the appropriate recovery path.
- Limit reached: stop predictably rather than allowing uncontrolled continuation; return a status the caller can distinguish from successful completion.
Retrying every failure is not a safe default. A retry may repeat a side effect, and a model may make a different request on the next turn. Define which failures are retryable, whether an operation is safe to repeat, and when the run should instead stop or ask for human intervention.
What should a runtime trace show?
A final answer alone rarely explains why a run went wrong. A useful trace lets an engineer follow the workflow: model calls, tool calls and their outputs, handoffs, guardrail events, and relevant custom spans or status changes. OpenAI’s tracing documentation describes these kinds of recorded events, while its trace-grading guidance covers using traces to identify workflow issues and regressions.
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When debugging, follow the run in order and locate the first point where it diverged from the intended behavior. Check whether the model requested the right operation, whether the runtime validated and routed it as expected, whether the tool returned a usable result, and whether the next step handled that result correctly. Also inspect pauses, errors, and guardrail outcomes; a plausible final response can conceal a failed or incomplete operation.
Logs and traces should support diagnosis without needlessly exposing sensitive data. Decide what inputs and tool results are recorded, who can access them, and how long they are retained. Trace detail is useful only if it is appropriate for the data and risk profile of the application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you evaluate an agent beyond its final answer?
Evaluate both the interaction and the task outcome. A transcript can show what the agent said and which steps it attempted; the final environment state shows whether the requested change actually happened. Those can differ: an agent may claim an action succeeded even when the underlying state did not change. For multi-step tasks, an early mistake can also alter the state in ways that affect later turns.
Build evaluations around concrete tasks and observable criteria. For each task, decide what a successful result looks like in the environment, which behaviors or constraints matter, and how a grader will check them. Graders can assess task outcomes as well as relevant parts of the interaction; a fluent answer alone is not an adequate success criterion for an agent that acts on tools.
The Tool Desk
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- Define success before running the agent. Specify the expected state change or other task criterion, and identify unacceptable actions or outcomes.
- Run the same tasks repeatedly when outputs can vary. Anthropic’s evaluation guidance calls separate attempts trials and notes that model outputs vary between runs. Multiple trials help reveal inconsistency that a single run can miss.
- Inspect failures and preserve them as test cases. Use representative traces to understand what failed, then add repeatable evaluations for those behaviors.
- Compare changes on a consistent set. OpenAI recommends using datasets and repeatable eval runs to compare changes over time, including prompt or workflow changes.
Track more than whether the answer reads well. Depending on the task, useful criteria may include correct tool selection, valid handling of tool results, adherence to approval boundaries, verified task completion, and appropriate behavior when the run cannot complete. The evaluation should reflect the consequences of the application’s actions, not an invented universal reliability percentage.
How should you compare agent implementations?
There is no established universal winner or controlled cross-vendor benchmark in the cited guidance. Compare implementations against your system’s requirements instead:
| Decision axis | Questions to answer |
|---|---|
| Tool execution | Does your application execute tools, or does a hosted service? Who handles authorization, network access, approvals, errors, and returned results? |
| State and resumption | Is state held in application history, an SDK session, or a server-managed conversation? How is a paused run resumed? |
| Observability | Can you inspect model calls, tool calls and outputs, handoffs, guardrails, and workflow status in a run? |
| Evaluation | Can you grade task outcomes and traces, run multiple trials, and repeat evaluations to compare changes? |
| Control and operational burden | Which responsibilities remain with your application, and which move to a hosted service? What control or visibility is exchanged for that shift? |
Anthropic’s evaluation guidance, published January 9, 2026, emphasizes tasks, trials, graders, transcripts, outcomes, and the evaluation harness. Together with runtime and tracing guidance, it points to a practical standard: judge an agent by the behavior of the complete system across turns, including its verified outcomes and failure paths.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches
