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An incorrect or incomplete agent answer is usually a pipeline failure, not simply a bad prompt. First preserve the exact failing run, then inspect the complete trace from request through retrieval, tool calls, handoffs, guardrails and final response. Classify the first faulty transition as an instruction/model, retrieval/context, tool/orchestration, or validation problem. Change only that layer, rerun the same case repeatedly, and then run a representative evaluation set before deploying the fix.
This method also separates a plausible-sounding answer from a trustworthy one. It records where evidence came from, what tools did, which permissions and guardrails applied, and whether a human must approve the resulting action.
What should I do first when an AI agent is wrong?
Stop treating the response as an isolated text error. Preserve the complete execution and make the failure reproducible before editing a prompt, swapping a model, or adding documents.
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- Repeat the same case. Agent behavior is probabilistic, so run the identical input more than once. A successful retry shows only that the failure is variable; it does not prove a fix.
- Trace the first bad transition. Follow the request through routing, planning, retrieval, tool calls, handoffs, guardrails and final generation. The earliest incorrect decision usually identifies the layer to repair.
- Apply one targeted change. Change only the lever implicated by the trace, then rerun the frozen case and the full evaluation set.
OpenAI’s agent-evaluation guidance starts with traces and then moves to datasets and repeatable evaluation runs. That order prevents random prompt tweaking from masking the original defect.
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How do I freeze a reproducible failure?
A useful reproduction is a complete record of the inputs and state that could have influenced the answer. Without it, two apparently identical tests may actually use different retrieved content, memory, permissions or tool results.
Record the request and instructions
- The exact user message, including punctuation, attachments and any structured fields.
- The complete system and developer instructions in force for that run, including policy, formatting and escalation rules.
- Model name, model version, parameter settings and any routing choice made by the agent.
- Conversation history, persistent memory and session variables visible to the agent.
Record context and retrieval
- Every query issued to a search or vector store.
- Retrieved document identifiers, titles, versions, timestamps, ranking scores or positions, and the exact passages inserted into the context.
- Filters and metadata constraints, such as tenant, locale, access level or date range.
- Whether a source was missing, stale, contradictory or truncated before generation.
Record tools and workflow state
- The tool schema shown to the model, including required fields, allowed values and descriptions.
- Each tool name, argument payload, permission check, timeout, retry and returned output.
- Planner decisions, route changes, handoffs between agents and state passed between steps.
- Guardrail decisions, blocked calls, content transformations and fallback branches.
Record the final result
- The exact response shown to the user, including citations, confidence labels and omitted fields.
- Any post-processing, formatter, validator or redaction applied after generation.
- The action taken, if the response triggered an external change.
Store an immutable run identifier and timestamps for every event. Redact secrets and apply your organization’s retention policy, but do not remove fields needed to reconstruct the decision path.
What should I inspect in an end-to-end agent trace?
Read the trace in chronological order rather than jumping straight to the final text. Microsoft’s observability guidance calls for request identity, timestamps, run identifiers, retrieval provenance, tool names and arguments, permissions, outputs and linked end-to-end traces.
- Ingress and routing: Confirm that the intended agent, model and policy were selected for the request.
- Planning: Check whether the plan decomposed the task into all required subquestions or silently narrowed it.
- Retrieval: Verify the query, ranking, filters, source freshness and context length. Identify the first point where a necessary fact disappeared or an irrelevant passage entered.
- Tool execution: Compare the selected tool and arguments with the task. Check permissions, returned data, errors, retries and timeouts.
- Handoffs and memory: Confirm that the next agent received the right state, source references and constraints rather than a partial summary.
- Guardrails and validation: Determine whether a checker rejected, altered or accepted the result and whether an approval gate was bypassed.
- Generation: Compare the final answer with the evidence actually available to the model. Look for unsupported claims, missing required items and formatting violations.
The goal is to identify the first bad transition, not merely the last visible symptom. A polished final paragraph may be downstream of a wrong retrieval filter or a failed tool call.
How can I tell whether the prompt or model is the problem?
Classify a failure as instruction or model behavior when the needed information and tools were available, but the agent ignored constraints, selected the wrong priority, formatted incorrectly or behaved inconsistently.
Typical signals
- The trace contains the correct evidence, yet the response contradicts it or omits an explicitly required field.
- The same inputs produce materially different reasoning or formatting across repeated runs.
- The agent follows a lower-priority instruction over a higher-priority one, or fails to decompose a multi-part request.
Targeted repairs
- Rewrite instructions as explicit objectives, constraints, output fields and stopping conditions.
- Add concise examples for edge cases, conflicts and required formatting.
- Decompose a broad task into verifiable subtasks instead of asking one generation step to do everything.
- Try a model better suited to the reasoning, context or tool-use requirement, then measure the change rather than assuming a larger model is better.
Do not change retrieval or tool permissions for a pure instruction failure. Re-run the frozen case and the evaluation set after the single intervention.
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How do I diagnose retrieval or context failures?
A retrieval or context failure occurs when the needed fact was not supplied, a stale or irrelevant passage was supplied, or useful evidence was buried in an oversized context.
Check what the agent actually saw
- Was the user’s wording transformed into a useful search query?
- Did ranking return the authoritative passage, or merely a semantically similar one?
- Did chunk boundaries split the definition, exception or table row needed to answer?
- Did metadata filters exclude the right source or include documents from the wrong tenant or date?
- Did context limits truncate the relevant passage or bury it among low-value text?
Fix the implicated retrieval lever
- Improve query formulation, including synonyms and the entities or dates that disambiguate the request.
- Tune ranking and reranking so authoritative and current passages outrank merely similar text.
- Adjust chunk size and overlap to keep definitions, conditions and exceptions together.
- Use metadata for source authority, version, geography and access scope.
- Reduce irrelevant context and preserve the exact evidence spans needed for the answer.
Adding more documents is not automatically a fix. Extra, stale or conflicting passages can make a correct answer less likely if ranking and context selection remain unchanged.
Why can relevant sources still produce a false or incomplete answer?
This is a grounding and prompt-structure failure when relevant passages are present but the agent is not clearly required to use them, disclose gaps or resolve conflicts.
Symptoms
- The answer contains a source-related claim that the retrieved text does not support.
- The agent invents a detail instead of saying that the evidence is missing.
- Conflicting passages are blended into one confident statement without identifying the conflict.
- One part of a multi-part question is answered while other requested parts disappear.
Repairs
- Tell the agent which sources to use and require each material claim to be supported by retrieved evidence.
- Define a specific behavior for missing or conflicting information: state the gap, present the alternatives and request clarification when necessary.
- Separate evidence extraction from answer composition so the validator can inspect the facts before prose is generated.
- Require an explicit completeness check against the user’s requested fields.
Vague or unstructured retrieval-augmented prompts can produce false or incomplete answers even when retrieval itself worked. Treat evidence use as a testable behavior, not an assumption.
How do I debug an agent that used the wrong tool?
Inspect tool selection, arguments, permissions, results and state transitions as separate failure points. A correct plan can still fail because a schema is ambiguous, an argument is malformed or a tool returned an error that the agent ignored.
Tool-selection errors
- Compare the chosen tool with the user’s requested operation and the tool descriptions available at that step.
- Check whether a handoff sent the task to an agent that lacked the required capability.
- Look for premature completion after a search, calculation or verification step.
Argument and permission errors
- Validate required fields, types, allowed values, date formats and identifiers before execution.
- Confirm that the permission check covered the actual resource and action, not just the tool name.
- Distinguish an authorization denial from an empty successful result; the agent should not describe either as confirmed data.
Result and state errors
- Inspect raw tool output before any summarization or formatting.
- Check timeouts, retries and partial results. A retry may return a different record or duplicate an action.
- Verify that the next step received the complete output and relevant identifiers.
Repair the tool schema, permission boundary, timeout, retry policy or error handling named by the trace. Do not hide a tool failure by merely making the final prompt more persuasive.
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When is the problem validation rather than generation?
A validation failure occurs when the answer sounds plausible but contains an unsupported claim, omits a decision-changing risk or triggers an unsafe action. Validation means checking output after generation and before acting on it, sharing it or relying on it in a decision.
Use layered checks
- Structural: Are all required fields present, types valid and citations attached where required?
- Evidence: Does each important claim follow from an authoritative passage or an independent source?
- Consistency: Do totals, dates, identifiers and conclusions agree with the source and with one another?
- Policy and safety: Does the proposed action stay within permissions and guardrails?
- Human review: Is approval required because the action is high impact or difficult to reverse?
NIST describes evaluation probes as adversarial verifiers that can be integrated into agent workflows to check outputs, tool use and evidence. For high-impact decisions, an automated checker should support—not replace—the designated human approver.
How do I apply a fix without losing the ability to learn from the failure?
Use a one-change experiment. The trace determines the lever; changing several layers at once destroys attribution.
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- Select one intervention. Choose prompt wording or examples for instruction failures; query, ranking, chunking or freshness for retrieval failures; schema, permissions, timeout, retry or error handling for tool failures; or a checker and approval gate for validation failures.
- Rerun the frozen case repeatedly. Compare the same trace fields, not just whether one final answer looks better.
- Run the representative evaluation set. A local improvement that harms other task types is a regression.
- Record the result. Keep the changed configuration, scores, failure examples, latency, token use and logging impact.
Repeat this loop until the defect is fixed without creating a new failure class.
How do I build an evaluation set for incorrect or incomplete answers?
Create a dataset of representative questions and expected answers or task outcomes. Include ordinary traffic, edge cases, known failures, ambiguous requests, conflicting sources and tool-error scenarios.
Score the dimensions that matter
| Dimension | What to check | Useful evidence |
|---|---|---|
| Functional correctness | The requested task or outcome is completed accurately. | Expected result, identifier or state transition. |
| Instruction following | Required format, constraints, priorities and escalation rules are obeyed. | Field-by-field or rule-by-rule checks. |
| Accuracy | Claims agree with authoritative facts. | Reference answer and source comparison. |
| Relevance | The response addresses the request without distracting material. | Human or rubric-based judgment. |
| Thoroughness and completeness | All decision-relevant parts of a multi-part request are covered. | Checklist of required entities, conditions and exceptions. |
| Groundedness | Material claims are supported by the supplied evidence. | Claim-to-passage alignment. |
| Tool-use correctness | The right tool, arguments, permissions and sequence are used. | Trace grader over calls and outputs. |
OpenAI’s evaluation examples include checking whether an agent returns the correct tracking number and follows required instructions. Use trace graders to inspect workflow behavior, then dataset runs to compare prompts, routing, retrieval, models and guardrails over time.
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Make evaluation resistant to lucky retries
- Run each case enough times to expose variable behavior rather than accepting one successful sample.
- Keep a regression set of every production failure and every repaired case.
- Separate automated checks from human review for nuanced quality, safety and completeness judgments.
- Compare score changes with operational cost: latency, token consumption and logging volume.
What should production observability include?
Uptime, latency and error rate describe service health but do not show whether an answer is correct. Add AI-specific traces, quality evaluation and behavioral monitoring.
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Minimum production signals
- Correlated request, run and trace identifiers with timestamps.
- Retrieval queries, source identifiers, versions, provenance and selected passages.
- Every model decision that routes, calls a tool, hands off or stops.
- Tool arguments, permissions, outputs, retries, timeouts and errors.
- Memory reads and writes, guardrail outcomes and validation results.
- Quality and safety scores, user corrections and representative examples for review.
OpenTelemetry GenAI semantic conventions can provide a common structure where they fit your stack. Establish behavioral baselines and alert on anomalies such as unusual tool sequences, source changes, permission escalations, rising unsupported-claim rates or sudden completeness drops.
The Canadian Centre for Cyber Security recommends monitoring prompts, tool calls, memory interactions, decisions, actions, identity and privilege changes, source usage and behavioral anomalies. Apply access controls and retention limits to these logs because they may contain sensitive prompts and business data.
How should I validate important answers before trusting them?
Validate material claims against an authoritative source or multiple independent sources, especially when the answer affects money, safety, access, compliance or an irreversible action.
- Identify the claims that change a decision. Do not spend equal review effort on explanatory wording and the one date, amount, permission or condition that determines the outcome.
- Check the evidence. Confirm that the cited or retrieved passage actually entails the claim and is current for the relevant geography, edition or policy version.
- Seek independence when stakes are high. A second passage copied from the same underlying source is not independent confirmation.
- Use a verifier. An evaluation probe can challenge the answer, inspect tool use and demand evidence before release.
- Require approval for high-impact actions. Present the proposed action, evidence, uncertainty and reversibility to the authorized human before execution.
Models can be incorrect, fabricate citations and sound confident while wrong. Treat confidence language as a presentation feature, not proof.
Which fix should I choose when several layers look suspicious?
Choose the intervention based on the earliest confirmed failure and compare alternatives using evidence, repeatability, safety and cost.
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| Failure stage | Primary intervention | Evidence to compare | Operational trade-offs |
|---|---|---|---|
| Prompt or model | Instructions, examples, decomposition or model selection. | Instruction-following, accuracy and consistency across repeated runs. | Model changes can affect latency and token use. |
| Retrieval or context | Query formulation, ranking, chunking, metadata and source freshness. | Provenance, passage relevance, groundedness and completeness. | More retrieval or context can increase latency, tokens and logging volume. |
| Tool or orchestration | Schema, permissions, timeout, retry, state transfer or error handling. | Correct tool sequence, arguments, outputs and handoffs. | Retries and additional checks can delay actions; permissive access increases risk. |
| Validation or action | Output checker, independent verification, anomaly handling and human approval. | Unsupported-claim rate, safety outcomes and approval decisions. | Review adds operational cost but reduces high-impact errors and irreversible actions. |
If the trace does not establish a root cause, collect better traces before making a broad change. A bigger model, a longer prompt and more documents together may improve one example while leaving the underlying defect unknowable.
What common troubleshooting mistakes make agents less reliable?
- Editing the prompt before saving the trace: You lose the evidence needed to distinguish retrieval, tool and model behavior.
- Testing one successful retry: Probabilistic behavior can make an unfixed defect appear resolved.
- Judging only the final prose: A fluent answer can conceal a wrong tool call, stale source or omitted risk.
- Adding documents without inspecting ranking: More context can bury the evidence or introduce conflicts.
- Logging only uptime and errors: A system can be available while its answers are consistently wrong.
- Changing several levers at once: You cannot attribute an improvement or a regression.
- Accepting citations without checking them: A citation can be fabricated or fail to support the sentence beside it.
- Allowing automatic high-impact actions: The cost of an unreviewed error may exceed the cost of a human approval step.
What is a practical release checklist?
- Reproduce each known failure with the original inputs and state.
- Capture a complete, correlated trace from ingress to final action.
- Identify the first faulty transition and classify its layer.
- Apply one targeted change and retain the before-and-after configuration.
- Run repeated trials of the frozen case.
- Run the full representative evaluation set and regression suite.
- Review correctness, instruction following, accuracy, relevance, thoroughness, completeness, groundedness and tool-use correctness.
- Check latency, token use, logging volume and any new permission or safety exposure.
- Verify high-impact claims against authoritative or independent sources.
- Keep human approval gates for actions that are high impact or difficult to reverse.
- Deploy production traces, quality baselines and anomaly alerts before widening access.
This process turns an agent that occasionally sounds wrong into a system whose failures can be located, measured, repaired and safely monitored.
Frequently Asked Questions
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It may reduce variation between runs, but it cannot supply a missing document, repair a bad tool argument or enforce evidence use. Treat sampling settings as a model-behavior experiment and verify the result on the same evaluation set.
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No. Oversized context can bury the useful passage, increase token cost and introduce conflicting or stale evidence. Retrieve focused, current passages and measure ranking, provenance and completeness instead.
What if an agent cites a source that does not support its claim?
Classify that as a grounding or validation failure, even if the source itself is genuine. Require claim-to-passage checks and instruct the agent to disclose when the available evidence is insufficient or conflicting.
How can I test an agent that changes records or sends messages?
Run it in a sandbox or dry-run mode where possible, capture the proposed action and arguments, and compare them with an expected outcome. Keep an authorized human approval gate for actions that are high impact or hard to reverse.
When should an evaluation set be rerun?
Rerun it after any prompt, model, routing, retrieval, tool, permission, memory or guardrail change. Also schedule checks for behavioral drift and run the regression cases whenever production monitoring reveals a new failure.
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