The Tool Desk
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When an AI agent makes the wrong move or appears stuck, do not start by rewriting the prompt or clicking Retry. Preserve the run trace, identify the first point where the agent departed from the intended plan, classify the failure, and change only the condition that caused it. This approach separates prompt problems from bad tool schemas, misunderstood outputs, blocked permissions, transient service errors, and client or network failures. It also prevents a retry from repeating a file change or external action that already succeeded. A comparable rerun, followed by trace inspection and a regression test, is the reliable path from a confusing symptom to a fix.
What should I do first when an AI agent fails?
Freeze the run and preserve its evidence before intervening. A failed turn can already have modified files, sent a request, or called an external tool, so blindly retrying can create duplicate side effects or make the original cause harder to see.
Capture the complete run state
- The exact user request, including attachments, variables, and any prior conversation that supplied context.
- The model name, model version, agent or SDK version, system instructions, temperature or other generation settings, and tool configuration.
- Every tool definition, required field, enum, permission, timeout, and validation rule active during the run.
- Tool inputs and outputs exactly as transmitted, with timestamps and request or correlation IDs when available.
- Terminal output, changed-file lists, database or API responses, approval events, guardrail decisions, and other external side effects.
- The visible symptom and the expected result, written as separate statements.
Keep the original trace immutable. Work from a copy or a new run that points to the preserved record. OpenAI’s error guidance warns that a failed turn may have completed part of its work; verify the state of files, records, messages, and remote jobs before allowing the agent to repeat an action.
How do I find the first wrong decision in an agent trace?
Read the trace from the beginning, not from the last error. The earliest incorrect assumption, malformed call, skipped step, or blocked action is usually the point where the eventual failure became inevitable.
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Follow the intended plan step by step
- Write the intended sequence in plain language: for example, inspect the input, query the source, validate the result, then make the change.
- Mark each model generation, tool call, handoff, guardrail event, approval request, and returned result in the trace.
- At every transition, ask whether the agent had the information and permission required for the next step.
- Stop at the first mismatch. Do not choose the most dramatic error message if an earlier incorrect assumption caused it.
- Compare the state immediately before and after that transition, including changed files, terminal output, and tool arguments.
VS Code’s recovery guidance recommends reviewing changed files, recent tool calls, terminal output, and error messages while locating the earliest divergence. An agent that eventually reports “nothing found,” for example, may have used the wrong directory several calls earlier; correcting the final message would not repair the workflow.
Record a precise divergence statement
Use a sentence with four parts: “At event X, the agent did Y; it should have done Z; the evidence is W.” This prevents a vague diagnosis such as “the model was confused” and gives you a testable target for the next run.
What kinds of AI-agent failures should I distinguish?
Microsoft Research’s AgentRx taxonomy identifies nine useful categories. Classification matters because each category calls for a different intervention; adding more prompt text cannot repair a revoked credential, and changing a tool schema cannot resolve an ambiguous user goal.
- Plan Adherence Failure: the agent understood the objective but skipped, reordered, or abandoned a required step.
- Invention of New Information: the response contains facts, records, or tool results that were not supplied or retrieved.
- Invalid Invocation: a tool call has the wrong name, argument type, missing required field, invalid enum, or otherwise fails validation.
- Misinterpretation of Tool Output: the tool returned usable data, but the agent read its meaning incorrectly.
- Intent–Plan Misalignment: the proposed plan does not actually achieve the user’s stated goal.
- Under-specified User Intent: several reasonable actions fit the request and the agent guessed instead of clarifying.
- Intent Not Supported: the requested outcome is outside the agent’s available capabilities or tools.
- Guardrails Triggered: a safety, policy, authorization, or approval control intentionally blocked the action.
- System Failure: credentials, service availability, networking, sandboxing, endpoint configuration, or another infrastructure dependency failed.
AgentRx was reported by Microsoft Research on March 12, 2026 using 115 manually annotated failed trajectories. Its benchmark announcement reported a 23.6% improvement in failure localization and a 22.9% improvement in root-cause attribution over prompting baselines. Those percentages describe that benchmark and comparison; they are not a guarantee for every agent or model.
How do I choose the right debugging approach?
Use the deepest evidence that is safe to collect, then make the smallest intervention that can explain the divergence. A final answer alone is useful for spotting a symptom, but it rarely tells you whether the model selected the wrong tool, supplied a bad argument, misunderstood a result, or was blocked by policy.
| Approach | Evidence depth | Failure localization | Reproducibility | Side-effect control | Regression coverage |
|---|---|---|---|---|---|
| Final-output review | Final response only | Last visible error | Single rerun | Low unless actions are manually checked | Single reproduction |
| Trace review | Generations, tool calls, handoffs, guardrails, and custom events | Earliest divergence | Comparable rerun with captured configuration | State is checked before retrying | Repeated cases can be compared |
| Targeted intervention | Trace plus one changed prompt, schema, tool, permission, or infrastructure condition | Change is tied to the diagnosed cause | One-variable rerun | Retry path avoids the failed action | Reproduction becomes a test case |
| Trace-graded evaluation | Structured scores or labels for end-to-end decisions, tool calls, and reasoning steps | Patterns across many traces | Fixed dataset and consistent graders | Evaluations can use isolated or mocked tools | Graded evaluation set |
OpenAI describes trace grading as the fastest way to identify workflow-level issues because it can show whether an agent chose the right tool, handed off correctly, or violated an instruction. The Agents SDK records model generations, tool calls, handoffs, guardrails, and custom events that support this analysis.
How do I fix a prompt, plan, or intent problem?
Change the instructions only when the trace shows a planning or intent failure. Make the goal, constraints, acceptance criteria, and required sequence explicit, then rerun the same case so you can tell whether the change mattered.
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Plan Adherence Failure
State the required order and the condition that must be true before each step. Add an explicit completion check, such as “do not edit until the target file and requested lines have been confirmed.” If the agent is allowed to choose among alternatives, define which choices are acceptable rather than presenting a rigid sequence that conflicts with the task.
Intent–Plan Misalignment
Compare the plan’s proposed outcome with the user’s actual success condition. A plan to summarize a dataset is not aligned if the user asked for a validated export with a particular schema. Rewrite the acceptance criteria in observable terms and require the agent to restate them before taking a consequential action.
Under-specified User Intent
Do not force a guess when two actions could produce materially different results. Ask one focused question that resolves the blocking choice, such as the target environment, date range, file to modify, or whether a destructive operation is permitted. Preserve the original request and the answer together in the next trace so the clarification is auditable.
Intent Not Supported
Explain the capability boundary instead of instructing the agent to improvise. If the desired result is legitimate and important, add a tool or a human approval step that supplies the missing capability; otherwise, offer the closest supported outcome and state what it cannot do.
How do I stop an agent from inventing facts?
First confirm that the claimed information is absent from both the conversation and tool results. Then require grounding in retrieved or tool-returned data and make the supporting evidence visible in the trace.
- Separate “known from input,” “returned by tool,” and “inferred” fields in the agent’s working state.
- Require a citation, record identifier, or quoted field for claims that affect a decision.
- Permit an explicit “not found” or “insufficient information” result instead of rewarding a complete-sounding guess.
- Add a validation step before an external action, checking that every critical value exists in the returned data.
Do not solve an invention problem by merely increasing output length. More prose can hide the unsupported assertion; provenance and a refusal path make it detectable.
How do I debug an invalid or failed tool call?
Inspect the exact serialized arguments and the tool’s schema at the moment of invocation. An invalid invocation is usually repaired at the interface boundary, not by asking the model to “try harder.”
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Check the contract
- Confirm the tool name and operation are available in the current environment.
- Mark every required field and reject missing, null, or empty values where they are not valid.
- Use strict types and enumerated values for dates, modes, identifiers, and status fields.
- Return validation errors that identify the field, expected type or value, and whether the operation was executed.
- Keep the schema and the implementation synchronized across agent versions.
If validation failed before execution, record that fact distinctly from a tool that ran and returned an application error. This distinction determines whether a retry is harmless or could repeat an external action.
What should I do when the agent misreads a tool result?
Make the result structured, typed, and unambiguous, then add a check before any consequential action. A correct tool response can still lead to a wrong outcome when labels are vague, units are implicit, or an empty result is interpreted as success.
Improve the response contract
- Return named fields with explicit units, time zones, status values, and null semantics.
- Distinguish an empty list, an error, a partial result, and a successful result with zero matches.
- Include the source identifier and query parameters that produced the result.
- Require the agent to summarize the relevant fields and verify them against an acceptance rule before proceeding.
For high-impact operations, insert a confirmation or deterministic validator between interpretation and execution. The validator should reject an unsafe or incomplete state rather than relying on another free-form model response.
How do I recover an AI agent that is stuck or looping?
Determine whether the agent is waiting for something, still working, or actively repeating a mistake. The recovery action differs in each case.
When it is waiting
Look for a pending clarification question, approval request, permission dialog, or terminal command that has not returned. Supply the missing answer or approval only after confirming that the request is legitimate and that the command is operating on the intended target.
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Check the process, job status, tool timeout, and latest trace timestamp. A long-running operation may be progressing normally, while an unchanged timestamp can indicate a dead connection or crashed worker. Avoid launching a second copy until you know whether the first job has side effects.
When it is active but wrong
Intervene as soon as you recognize a mismatch, as VS Code guidance recommends. Give a focused correction tied to the observed event—such as the correct directory, record, or tool—and require the agent to verify the corrected state before continuing.
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When it is looping
Stop automatic retries and capture the repeated sequence. Ask for the exact failed command and the first error, verify prerequisites, and choose a recovery path that does not repeat the failed action. Add a loop limit, progress condition, or explicit escalation to a human when the same state recurs.
When is it safe to retry an AI-agent failure?
Retry only failures that are plausibly transient and only within a bounded policy. OpenAI identifies rate limits, overloads, timeouts, and temporary service failures as retry candidates; invalid input, authentication failures, and configuration errors should be fixed before retrying.
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- Set a maximum number of attempts or an absolute deadline.
- Honor a server-provided
Retry-Aftervalue when present. - Use increasing delays for repeated transient failures rather than sending a burst of identical requests.
- Attach an idempotency key or equivalent deduplication control to operations that might reach an external service.
- After the limit, surface the preserved error and a recovery option instead of silently continuing.
Before any retry, confirm whether the prior attempt created a file, sent a message, started a job, or changed a record. If completion is uncertain, query the system of record first and reconcile its state.
How do I tell whether a hang is caused by the client or network?
When the trace stops without a meaningful tool or model error, investigate the service and client path separately from the agent logic.
- Check the provider’s service-status page for an incident or degraded component.
- Refresh the session or start a new chat, preserving the original trace first.
- Temporarily disable browser extensions and VPN, proxy, or security filters that can interrupt streaming or authentication.
- Try a different supported browser and network to isolate a local path problem.
- If the issue persists, collect a HAR file and browser-console errors with timestamps, then provide them to support or the platform administrator.
A client or network diagnosis should not be used to explain a malformed tool argument. Match the evidence: transport interruptions leave connection or timing clues, while schema failures appear at the invocation boundary.
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Change one relevant condition and repeat a comparable request. A successful answer after changing the prompt, model, tool, dataset, and network simultaneously does not identify which change repaired the failure.
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- Compare the first-divergence event with the original trace.
- Confirm that the corrected tool arguments, handoff, guardrail result, or infrastructure status appear as expected.
- Check the user-visible result against the acceptance criteria, not just the absence of an error.
- Verify external state independently so a plausible response is not mistaken for a completed action.
Keep both traces and label the changed condition. If the failure is nondeterministic, repeat the fixed case enough to establish a stable pattern and record the remaining variance rather than declaring a single lucky run a solution.
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How do I prevent the same agent error from returning?
Turn the recovered failure into a representative evaluation case. OpenAI’s trace-grading guidance recommends structured scores or labels for end-to-end decisions, tool calls, and reasoning steps so teams can compare changes and find workflow-level regressions.
Build a regression record
- Store the original request, relevant configuration, expected behavior, and the first-divergence label.
- Include the smallest safe fixture or mock that reproduces the failure without touching production data.
- Grade the specific behavior: correct tool selection, valid arguments, grounded claims, required handoff, guardrail compliance, or final state.
- Run the case after prompt, schema, model, tool, permission, and infrastructure changes that could affect it.
- Keep a separate test for the recovery path, including timeout, denied permission, empty result, and partial-completion conditions where applicable.
A regression set should represent real failure classes rather than only successful examples. Over time, the labels reveal whether a change moves errors from one category to another, such as replacing invalid invocations with misinterpreted outputs.
What evidence should an operations team retain?
Retain enough context to reconstruct the decision without retaining secrets unnecessarily. Access-controlled traces, redaction of credentials and personal data, and documented retention periods protect both debugging value and user privacy.
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- Immutable event order and timestamps.
- Model and agent versions plus configuration hashes.
- Tool schemas and the exact inputs and outputs, with secrets redacted.
- Permission, approval, and guardrail decisions.
- External identifiers and final state checks for side-effecting operations.
- The diagnosis, intervention, comparable rerun, and regression-test result.
Do not edit a trace to make it easier to read. Add an annotated copy that points to the original events so later reviewers can distinguish observed evidence from interpretation.
What is the practical troubleshooting sequence?
Use this compact sequence whenever an agent makes a wrong move, fails a tool call, or appears frozen:
- Freeze execution and inspect completed side effects.
- Preserve the request, configuration, tools, permissions, timestamps, and trace.
- Locate the first divergence from the intended plan.
- Assign one of the nine failure categories.
- Apply the narrowest fix at the prompt, schema, tool, permission, or infrastructure layer.
- For a hang, check pending input, approvals, running commands, service status, and the client/network path.
- Retry only a bounded transient failure, honoring
Retry-Afterand avoiding duplicate actions. - Rerun a comparable case with one changed condition.
- Verify both trace behavior and external state.
- Convert the failure into a graded regression test.
This sequence keeps diagnosis evidence-led: it explains not only what the agent said, but where the workflow stopped matching the user’s intent and how to prove that the repair lasts.
Frequently Asked Questions
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Resetting can remove useful context and make the failure impossible to reproduce. Save the trace first, then use a fresh session only as a controlled comparison with the same request and configuration.
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How can I debug an agent that fails only occasionally?
Capture several traces and compare the first divergence, timing, tool responses, and model configuration. Intermittent failures often need a fixed fixture, correlation IDs, and a graded test that records frequency rather than relying on one successful rerun.
Can better prompting eliminate all AI-agent mistakes?
No. Prompts can reduce plan and intent errors, but schema defects, unavailable services, permission blocks, misunderstood outputs, and transient network problems require fixes outside the prompt. Treat prompting as one intervention layer in the trace-based workflow.
What should I do if a failed run may have changed production data?
Pause further retries and query the authoritative system of record. Reconcile the actual state, identify any duplicate or partial action, and use an idempotent recovery procedure or human approval before making another change.
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