The Tool Desk
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An AI agent can return a polished answer and still fail the task. To find out why, inspect the run from its first decision to its final effect: prompts, retrieved context, tool choices and results, orchestration, policy checks, and any outside dependencies can all contribute. Start with the trace, locate the first meaningful error, and test a change against a defined measure of task success.
Why can an agent fail when its answer looks right?
A plausible final response is not proof that the agent did what the user asked. A transcript records what the system said and did; the outcome is whether the task was actually completed. If the agent was supposed to change something in an environment, verify that the intended state change happened rather than relying on a completion claim.
OpenAI’s agent evaluation guidance describes a trace as an end-to-end record of model calls, tool calls, guardrails, and handoffs for one run. That sequence matters: a mistaken early decision can shape later actions, and the final answer may conceal where the run went off course. Anthropic’s agent-evaluation guidance, published January 9, 2026, also describes how errors can propagate across turns and state changes.
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Think of the model as one component in a system. The prompt establishes instructions and scope; retrieval supplies context; tools expose actions and information; orchestration controls routing and handoffs; policy checks constrain behavior; and external services can fail or stall. A failure in any of these layers—or a combination of them—can produce the same outward symptom.
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How to debug an AI agent, step by step
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Reproduce and preserve the run
Keep the user input, model and version, relevant context, ordered actions, tool arguments and results, retrieved material, errors, guardrail decisions, latency and cost, and the final state when applicable. Preserve action order so you can see whether an early mistake drove later steps.
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Find the first meaningful divergence
Walk through the trace and ask where the run first stopped matching the task: Was relevant evidence missing or buried in noise? Did the agent select the wrong tool or supply a bad argument? Did a tool or API fail? Did routing send the task down the wrong branch? Were instructions conflicting or unclear about scope? Did a policy check miss a risky action?
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Form a testable hypothesis
Choose the layer that best fits the evidence, then change one relevant thing where practical. For example, test retrieval changes if the supplied context is incomplete or noisy; revise tool descriptions if their purpose or boundaries are ambiguous; or make routing and state transitions more explicit if control flow is the problem.
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Measure task success, not confidence of tone
Define what counts as success for the task and how it will be graded. For an action that changes an environment, check the resulting state. For a research or response task, grade whether the answer meets the relevant requirements. Compare the same representative cases before and after the change.
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Turn recurring failures into regression tests
Keep representative tasks and convert recurring incidents and domain-expert corrections into evaluation cases. Run multiple trials when outputs vary, and compare prompt or workflow changes using datasets and evaluation runs rather than intuition alone.
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Monitor production behavior
Track task quality alongside tool calls, errors, latency, token use or cost, and user feedback. Where possible, connect negative feedback to the specific trace so the failure can be investigated rather than treated as an isolated rating.
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What the trace shows—and what to try first
| Trace evidence | Layer to inspect | First response to evaluate |
|---|---|---|
| The task needs evidence, but the context is missing, irrelevant, or noisy. | Retrieval and context | Inspect retrieved results for relevance and noise, and whether enough useful material reaches the model. |
| The relevant evidence is present, but the agent interprets it incorrectly or behaves inconsistently. | Prompt and model behavior | Improve instructions and examples, then evaluate the same cases. Consider fine-tuning only when the issue is demonstrated task behavior or consistency and a representative dataset is available. |
| The agent chooses the wrong tool or produces incorrect arguments. | Tool interface, tool selection, or model behavior | Clarify each tool’s purpose, boundaries, and examples; grade both tool choice and argument correctness. |
| A tool, API, or dependency returns an error or stalls. | Tool integration and runtime | Inspect inputs, outputs, error states, and latency; evaluate bounded recovery where appropriate. |
| The agent takes the wrong branch, hands off incorrectly, or repeats a loop. | Orchestration and control flow | Inspect routing and handoffs. Use code-driven transitions where predictable control is important. |
| Untrusted web content or other outside material influences an unsafe action. | Policy, permissions, and runtime controls | Treat outside content as untrusted; examine guardrails and tool authority, with checks suited to the risk of the action. |
These are starting points, not guaranteed fixes; more than one layer can be involved. OpenAI’s accuracy guidance distinguishes retrieval failures, such as wrong or excessive irrelevant context, from cases where the model receives the right context but still uses it incorrectly. That distinction helps prevent tuning the prompt or model to compensate for a retrieval problem.
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Tool choice depends partly on how clearly the interface communicates what each tool does and when to use it. If descriptions are vague or tools overlap, the agent may map a user’s intent to the wrong action or confuse one tool’s boundaries with another’s. Partnership on AI’s 2025 report identifies ambiguous or overlapping tool interfaces and interpretation of user intent among operational risks.
Inspect the tool definition and the trace together. Make the tool’s purpose, appropriate use, limits, inputs, and expected outputs distinguishable from neighboring tools. Add examples where they resolve a genuine ambiguity, then evaluate whether the agent selects the appropriate tool and supplies valid arguments on representative requests. If the tool was selected correctly but failed during execution, investigate its runtime path instead of treating the incident as a selection error.
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When should you change the prompt, retrieval, tools, or model?
- Change retrieval when the context reaching the model is missing, irrelevant, or overloaded with noise.
- Change instructions or examples when the necessary context is available but the agent misunderstands scope, requirements, or expected behavior.
- Change tool descriptions or interfaces when tool purpose, boundaries, or expected arguments are unclear, or when tool choice is wrong.
- Change orchestration when routing, handoffs, branching, or loops cause the failure.
- Investigate runtime and dependencies when a tool or service errors, returns an unexpected result, or stalls.
- Consider fine-tuning when evaluation shows a task-behavior or consistency problem and you have representative examples. OpenAI’s accuracy guidance treats retrieval tuning, prompt engineering, and fine-tuning as distinct interventions rather than interchangeable remedies.
Make the smallest change that addresses the observed cause, then rerun the same evaluation cases. If several layers change at once, it becomes harder to know which intervention helped or introduced a regression.
Which orchestration pattern fits a multi-agent workflow?
OpenAI’s Agents SDK documentation describes two common patterns. The choice is about who owns control after a specialist is involved, not a guarantee that adding agents will make a workflow more reliable.
| Pattern | Who retains control? | Use it when |
|---|---|---|
| Agents as tools | A manager agent calls specialists and remains responsible for combining the result. | One agent should own and assemble the final response. |
| Handoff | Control passes to a specialist, which becomes active for the rest of the turn. | The routed specialist should directly own the remainder of the work. |
The SDK documentation also notes that code-based orchestration can offer more predictable speed, cost, and performance. Whatever pattern you choose, include routing and coordination in the evaluation: each added decision or handoff is another part of the workflow whose behavior needs checking.
What should you evaluate and observe?
Evaluation should connect the steps in a run to the result that matters. A useful setup defines representative tasks, success criteria, and grading logic; preserves traces so failures are diagnosable; and compares evaluation runs when prompts, retrieval, tools, or orchestration change. Agent observability and evaluation systems can support trace retention, inspection of retrieval and tool steps, grading, and repeatable tests, but the core requirement is a reliable way to examine the run and judge the outcome.
For production, monitor task quality alongside operational signals. A rise in errors or latency may identify a failing dependency; a pattern of wrong tool calls may point to interface or selection problems; user feedback tied to traces can expose failures that aggregate metrics miss. Treat these signals as leads for diagnosis, not substitutes for verifying task outcomes.
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