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When an AI agent takes an inappropriate next step, the visible mistake is usually a symptom rather than a diagnosis. The underlying break may be a misunderstood request, a flawed plan, an unsuitable or malformed tool call, a misread result, an invented fact, an ignored plan, a permission boundary, or a failing service.
The dependable way to debug the run is to reconstruct its trajectory, compare every decision with the user’s goal, current state, tool contract, returned data and authorization, then identify the earliest consequential divergence. Fix that cause and evaluate the corrected case again; changing the prompt first often treats the symptom while leaving the failure mechanism intact.
What a “wrong next step” actually tells you
An agent’s next action is produced from several inputs: the user’s request, system and developer instructions, the agent’s current plan, conversation state, tool descriptions, tool results, policies and permissions. A defect in any one of those inputs can lead to the same outward behavior. For example, a request to “send the approved invoice” can result in the wrong customer being selected because the intent was ambiguous, because the plan omitted an identity check, because the lookup tool returned stale data, or because the agent misread a correct result.
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Treat the run as a chain of decisions. The debugging target is the first step at which the chain stopped being supported by the request, state, evidence or authorization. Later errors are consequences until the trace shows otherwise.
Separate planning, tool-use and execution failures
Three broad layers make an initial classification easier:
| Layer | What should happen | Characteristic failure | Evidence to seek |
|---|---|---|---|
| Planning | The agent translates the goal and constraints into an ordered, permitted approach. | The plan conflicts with the user’s objective, omits a required check or assumes unavailable information. | The request, constraints, plan or model response before the first action. |
| Tool use | The agent chooses an appropriate capability and calls it with valid arguments. | The wrong tool is selected, required fields are missing, or a schema and permission are violated. | Tool name, version or contract, serialized arguments, validation errors and authorization result. |
| Execution | The system carries out the approved plan and responds to actual outcomes. | The agent ignores its plan, misreads a result, causes an unintended side effect or exceeds an authorized boundary. | Returned content, the next model response, side-effect logs, policy decisions and endpoint status. |
These layers are not mutually exclusive. A bad plan can force a correct tool call to do the wrong thing, and a good plan can be derailed by a service outage. Classifying the earliest break prevents every incident from being labeled a vague “reasoning problem.”
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A practical failure taxonomy
The following categories combine the nine-category taxonomy described in Microsoft Research’s AgentRx announcement with operational examples from the Partnership on AI. They are a classification aid, not a claim that every deployment has exactly these modes.
| Category | Meaning | Diagnostic clue in a trace |
|---|---|---|
| Plan adherence failure | A required step was skipped or an extra, unplanned action occurred. | The recorded action sequence diverges from the stated plan without a documented reason. |
| Invention of new information | The agent adds or changes facts without support from the request or a tool result. | A claim or parameter appears with no corresponding evidence span. |
| Invalid invocation | A tool call is malformed, omits a required argument or violates its schema. | Validation, parsing or contract errors; arguments do not satisfy the tool definition. |
| Misinterpretation of tool output | The returned data is read incorrectly and drives a wrong assumption. | The result contains one value, while the next model response acts as if another value was returned. |
| Intent–plan misalignment | The plan itself does not represent the user’s goal or constraints. | The plan is internally coherent but would not satisfy the request as written. |
| Under-specified user intent | A necessary choice or fact was never supplied. | The agent must guess a recipient, date, scope, threshold or other material parameter. |
| Intent not supported | No available capability can perform the requested task. | The agent keeps planning around a function that no tool or integration provides. |
| Guardrails triggered | Safety, privacy, access or approval controls block the requested action. | A policy decision or authorization denial appears before the attempted side effect. |
| System failure | Connectivity, an endpoint, a dependency or the orchestration system fails. | Timeouts, transport errors, unavailable services or explicit endpoint failures. |
Partnership on AI describes planning, tool-use and execution examples as illustrative and non-exhaustive because architectures and deployment contexts change. Use the categories to organize evidence, then add deployment-specific classes such as stale data, race conditions or handoff loss when your traces demonstrate them.
Trace the run from request to side effect
Do not start by rewriting the prompt. First define what “correct” meant, then replay the evidence in order.
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- Write the intended outcome and boundaries. Record the user’s requested result, explicit constraints, actions that must not occur, information that is missing, and operations requiring confirmation or authorization. If the request leaves a material choice open, mark it as a clarification point rather than silently selecting a value.
- Identify the exact run. Pin down the session, turn, model or agent version, relevant configuration and any delegated child runs. A reproducible identifier prevents evidence from several attempts being mixed together.
- Reconstruct the trajectory. Read model responses, tool calls, handoffs and outcomes in chronological order. OpenAI tracing documentation describes traces as the steps within a turn, with inputs, outputs, duration and status; tool spans can include the tool name, arguments, result, outcome status and errors when those fields are recorded.
- Mark the first consequential divergence. At each step, compare the decision with the user’s goal, current state, prior result, plan and permissions. The first consequential divergence is the earliest point that makes the later wrong action likely or inevitable. A later malformed call may be secondary if an earlier plan already selected the wrong account.
- Classify before correcting. Decide whether the evidence shows an intent problem, a planning problem, a tool-selection or argument problem, an output-interpretation problem, a plan-adherence problem, a guardrail decision or a system failure. If the trace is incomplete, label the conclusion as unproven and identify the missing span.
- Apply one targeted change and replay. Clarify the requirement, improve a tool schema or description, add output validation, enforce an execution policy, repair endpoint reliability or ask for the missing information—whichever matches the evidence. Re-run the same failure case and check both the decision path and the final side effect.
Which trace fields matter most
Inspect fields in this order so assumptions can be compared with the evidence that supposedly supports them.
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| Inspection point | Questions to answer | What a mismatch means |
|---|---|---|
| User request and constraints | What outcome, scope, exclusions and confirmations were stated? What information was absent? | May indicate under-specified intent or an intent–plan mismatch before any tool call. |
| Relevant model response | What did the agent say it would do, and what assumptions did it expose? | Reveals an incorrect interpretation or unsupported inference. |
| Selected tool and arguments | Was this capability appropriate? Do arguments satisfy the contract, types, required fields and permissions? | Points to wrong-tool selection, invalid invocation or an authorization issue. |
| Actual tool result | What did the service really return? Was it complete, stale, empty, ambiguous or an error? | Separates a service problem from a later misinterpretation. |
| Next model response and action | Did the agent quote or otherwise reflect the result accurately? Did it follow the plan? | Shows output misreading, invented information or plan-adherence failure. |
| Delegated handoff | What context, constraints and state reached the child agent, and what came back? | Can expose lost instructions, scope changes or an incorrect handoff. |
| Status and error fields | Were there policy denials, timeouts, retries, partial failures or transport errors? | Distinguishes blocked execution and system failure from a model decision. |
A successful HTTP status only says that a service handled a request. It does not say that the returned content supported the next action. Compare semantic content—identifiers, quantities, statuses and timestamps—with the assumptions in the following model response.
Tracing systems generally assemble a trace after a turn ends. When enabled for an organization, trace data may be exported through an API as OTLP JSON. Preserve access controls and redact secrets before exporting or sharing traces; the trace is itself a record of user input, tool arguments and potentially sensitive results.
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How to choose the right correction
| Evidence-backed cause | Targeted correction | Verification test |
|---|---|---|
| The request leaves a material choice unresolved. | Ask a clarifying question or require a structured field before execution; do not let the model invent the value. | Run the case with the field absent and confirm that the agent pauses instead of guessing. |
| The plan omits a required check or violates a constraint. | Encode the constraint in the planning instruction, state representation or policy and require a pre-action checklist. | Use a trace where the check is necessary and verify its span precedes the side effect. |
| The requested capability is unavailable. | Expose an honest limitation and offer a supported alternative; do not add a fictional tool. | Confirm that unsupported requests terminate with a clear explanation and no side effect. |
| The wrong tool is selected. | Clarify tool descriptions, names, selection criteria and examples; remove overlapping or misleading capabilities. | Present near-identical tasks and inspect the selected tool, not only the final answer. |
| Arguments violate the contract. | Use strict schemas, typed validation, enumerated values and a rejection path that returns actionable errors. | Inject missing, extra and wrong-type fields and verify the call is rejected safely. |
| The result is misread or incomplete. | Return machine-readable status and provenance, validate required fields, and require the agent to reconcile empty or conflicting data. | Test empty, partial, stale and contradictory results; the agent should stop or recover explicitly. |
| The agent invents facts. | Require evidence references in the working state, prohibit unsupported values and gate high-impact actions on verified fields. | Remove the supporting record and check that the agent declines to assert the value. |
| A guardrail or permission blocks the action. | Make the boundary visible, provide a safe escalation or confirmation path, and keep denied actions non-destructive. | Replay with and without authorization and confirm the policy decision is logged. |
| An endpoint or dependency fails. | Implement bounded retries, timeouts, idempotency keys, fallback behavior and a user-visible failure state. | Simulate timeout and duplicate-delivery conditions and inspect that no unintended duplicate side effect occurs. |
Why changing the prompt is often the wrong first move
Prompt changes help when the trace demonstrates an interpretation or planning defect. They cannot supply a missing permission, create an unavailable integration, repair a timeout or make ambiguous user intent specific. They can also mask a schema problem by encouraging the model to format an invalid call more confidently.
Before editing instructions, ask four questions:
- Was the intended outcome unambiguous to a human reviewer?
- Did the agent have the information and capability required to achieve it?
- Did the selected tool receive valid arguments and return trustworthy data?
- Was the attempted side effect authorized under the deployment’s policy?
Only after those checks should you alter wording. Keep the change narrow enough that a replay can show which behavior changed.
Evaluate the fix locally and across repeated runs
A repaired trace is a regression test, not proof that the class of failure is gone. Save the original request, expected constraints, failure label, relevant evidence and the corrected outcome as an evaluation case. Run it against the changed agent and inspect the trajectory as well as the final result.
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Then evaluate at several levels:
- Individual agent: Did it interpret the request and maintain the required state?
- Handoff: Did delegated work preserve instructions, permissions and context?
- Tool: Were selection, arguments, returned data and error handling correct?
- Completed run: Did the full sequence achieve the intended result without an unauthorized side effect?
Normalize traces so equivalent events can be compared, and group failures by category, tool, model version, handoff and environment. A single run can look fixed while repeated runs reveal a recurring issue such as a skipped review step, a particular tool’s empty response or a race between two agents. The OpenAI cookbook’s traced-run workflow recommends this combination of local evaluation and population-level pattern analysis. Its worked EV-order example uses synthetic traces and saved labels; treat that setup as an illustration of method, not production evidence.
Track rates that reflect the path, not only completion: invalid invocation, unsupported claim, policy violation, unverified side effect, recovery after a tool error and first-divergence category. Keep denominator and sampling rules stable so a change in logging does not look like a change in reliability.
A worked diagnostic example
Suppose an agent is asked to reserve an available service appointment next week, using the customer’s preferred location and asking before any nonrefundable booking. It books a different location without confirmation.
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- The plan in the trace searches availability but never records a location filter or confirmation checkpoint. The first consequential divergence is therefore a planning failure, even if the later API call is perfectly formed.
- If the plan had included the filter but the tool arguments omitted it, the classification would shift to invalid invocation or wrong-tool use.
- If the tool returned “preferred location: no availability; alternate location: available” and the agent treated that as approval, the failure would be output misinterpretation or intent–plan misalignment.
- If the agent waited for confirmation but the booking endpoint committed despite a dry-run flag, the execution or system boundary—not the plan—would be the first break.
The corrective test should include unavailable preferred locations, an alternate location, a nonrefundable option and an explicit confirmation response. Inspect that the trace records the decision and approval before the commit call.
Operational checklist for every incident
- Capture the exact user request, system instructions, model or agent version and configuration.
- State the expected outcome, forbidden actions, missing information and authorization requirements.
- Collect the complete chronological trace, including delegated work.
- Compare each tool name and argument with its current contract.
- Compare each returned result with the assumptions in the next action.
- Locate and label the earliest consequential divergence.
- Separate planning, tool-use, execution, guardrail and system evidence.
- Choose one correction that addresses the observed cause.
- Replay the original case and add it to the regression set.
- Review repeated traces for population-level patterns and newly introduced side effects.
What current evidence can—and cannot—show
AgentRx reports results on 115 manually annotated failed trajectories in a Microsoft Research 2026 benchmark. The project reports a 23.6% absolute improvement in failure-localization accuracy and a 22.9% improvement in root-cause attribution over prompting baselines in its experiments. Those are project-reported evaluation results, not a guarantee for a particular model, trace format or production workload.
The broader lesson is methodological: outcome metrics alone are insufficient, and evidence-backed localization is more actionable than asking an agent to explain itself after the fact. Your deployment still needs trace completeness, privacy controls, reliable labels and tests that represent its own tools, permissions and failure costs.
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