An AI agent’s wrong next step is a symptom, not a diagnosis. It may have misunderstood the request, formed a bad plan, selected or called the wrong tool, misread a valid result, invented information, ignored its plan, hit a guardrail, or encountered a system failure. The dependable fix is to reconstruct the run, find the earliest consequential divergence, classify it from the recorded evidence, and then change only the component that caused it.
Why a wrong next step is difficult to diagnose
Agents act through a sequence of model responses, tool calls, returned data, handoffs and execution decisions. A final answer can look reasonable even when an earlier step was already wrong. Conversely, the visible mistake at the end may be only a consequence of a bad assumption several steps earlier.
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Microsoft Research’s AgentRx announcement captures the limitation of end-of-task scoring: “Traditional success metrics (like ‘Did the task finish?’) don’t tell us enough.” A task can finish while the agent skips a required review, acts on stale information or exceeds the user’s authorization. A failed task can also contain several independent mistakes, so inspecting only the last response does not identify the root cause.
Debugging therefore starts with the trajectory, not with a new prompt. Preserve the user’s request and constraints, inspect every consequential decision in order, and identify the first point where the run stopped matching the intended outcome, available evidence or permitted actions.
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The three layers where an agent can go wrong
Planning errors
Planning is wrong when the proposed sequence does not represent the user’s goal, omits a required step or assumes information that was never supplied. A plan can also conflict with permissions. For example, a request to draft a change may be turned into a plan that publishes it, even though publication was not authorized.
Planning errors occur before a tool is called, but they often become visible only when a later action has an unwanted effect. Compare the plan with the literal request, explicit constraints, the current state and any confirmation requirements.
Tool-use errors
Tool-use errors include choosing an unsuitable tool, supplying malformed or incomplete arguments, violating the tool’s schema, or using a valid tool for a purpose it cannot support. A successful call status does not make the choice correct. The returned content still has to support the next action.
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Execution departs from the plan when the agent skips a planned check, performs an unplanned action, misreads a tool result, ignores an authorization boundary or continues after a guardrail or endpoint failure. Delegation adds another execution boundary: a handoff can lose constraints, pass the wrong state or return a result that the parent agent interprets incorrectly.
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Nine practical failure categories
The following taxonomy is a diagnostic aid based on the nine categories described in Microsoft Research’s AgentRx announcement. It is not a claim that every deployment has exactly these modes. Partnership on AI likewise describes planning, tool-use and execution examples as illustrative and non-exhaustive because architectures and deployment contexts change.
| Failure category | What the trace may show | Targeted correction |
|---|---|---|
| Plan adherence failure | A required step was skipped, or an extra action appeared that was not in the plan. | Make required checkpoints explicit and enforce them in the execution policy or controller. |
| Invention of new information | The agent states or acts on facts that are absent from the request, tool output and recorded state. | Require evidence for material claims and reject unsupported values before action. |
| Invalid invocation | A tool call is malformed, omits a required argument or violates the declared schema. | Correct the schema or tool description and add argument validation before dispatch. |
| Misinterpretation of tool output | The returned result says one thing, but the next model response assumes another. | Normalize result formats, expose relevant fields clearly and validate assumptions against the result. |
| Intent–plan misalignment | The plan itself conflicts with the user’s goal, constraints or requested level of authorization. | Clarify intent representation and require a plan check against the request before execution. |
| Under-specified user intent | A necessary choice, value or constraint was never provided. | Ask a focused clarification question instead of guessing. |
| Intent not supported | No available tool or capability can perform the requested task. | Explain the capability boundary or offer an achievable alternative; do not fabricate completion. |
| Guardrails triggered | A safety, access or policy control blocks the proposed action. | Respect the boundary, report the blocked operation and request authorized input when appropriate. |
| System failure | A connectivity problem, unavailable endpoint or tool error interrupts the run. | Handle the error explicitly, retry only under a defined policy and avoid treating missing data as success. |
A trace-based troubleshooting workflow
Use this order for a single incident. Changing prompts before locating the first divergence can hide the real cause and make later comparisons impossible.
- Define the intended outcome and constraints. Write down what the user asked for, what must not happen, which information is missing, and which actions require confirmation or authorization. Include the relevant starting state. This separates an intent–plan mismatch from a tool malfunction.
- Reconstruct the execution in order. Start with the session and turn. Then review model responses, tool calls, returned results, delegated work and status or error fields. OpenAI’s tracing documentation describes traces as the steps within a turn; recorded fields can include inputs, outputs, duration, status, tool arguments, tool results and errors when available. If trace export is enabled for the organization, traces can be exported as OTLP JSON through the API.
- Find the earliest consequential divergence. For each step, compare the decision with the stated goal, current state, previous result, plan and permissions. Mark the first point at which the run no longer satisfies those conditions. Later mistakes may simply propagate that first wrong assumption. AgentRx is designed to localize a critical failure step and record evidence-backed violations.
- Classify the divergence. Decide whether the request was misunderstood, the plan was inadequate, a tool or argument was wrong, a result was misread, the plan was ignored, information was missing, execution was blocked, or a system condition failed. Do not label every problem a “reasoning issue”; the remedy depends on the category.
- Apply a targeted correction and evaluate it. Clarify intent, improve a tool schema, validate a result, add an execution constraint, repair endpoint reliability or ask for missing information according to the evidence. Re-run the known failure case, preserve the trace and add recurring failures to the evaluation set.
What to inspect in each trace span
Read the trace as a chain of claims and evidence. The following sequence prevents an attractive final response from concealing an earlier error.
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| Trace element | Questions to ask |
|---|---|
| User request and constraints | What outcome was requested? What actions were prohibited, conditional or subject to confirmation? What information was absent? |
| Relevant model response | What did the agent believe the goal and current state were? Did it introduce an unsupported assumption? |
| Selected tool | Was this tool capable of the requested operation? Was a safer or more appropriate tool available? |
| Arguments | Are required fields present, correctly typed and consistent with the request and permissions? |
| Actual tool result | What did the tool really return? Was it complete, stale, empty, ambiguous or an error that the agent treated as data? |
| Next model response and action | Does the next decision follow from the result, or does it assume a different value or status? |
| Delegate handoff | Were the goal, constraints, current state and required output passed to the other agent? Did the returned work satisfy that contract? |
| Status and error fields | Was the operation technically successful, blocked, timed out or partially completed? Does the content support the decision even when the status is successful? |
Inspect the content of a result, not only its transport status. An HTTP 200 or a successful tool status can still contain an empty list, a permission warning, stale data or a value that does not answer the question the agent asked. The diagnostic question is whether the recorded result supports the next action.
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How to distinguish similar-looking causes
Bad plan versus wrong tool
If the plan already calls for an action that conflicts with the user’s goal, the failure is intent–plan misalignment. If the plan is appropriate but the agent selects a tool that cannot perform it, classify it as tool selection or invocation failure. The trace should show whether the mismatch existed before tool selection.
Wrong result versus misread result
A tool or endpoint can return incorrect, incomplete or stale data; that is a system or tool-quality problem. If the recorded result is clear and correct but the next model response draws the wrong conclusion, the divergence is misinterpretation of tool output. Preserve both the raw result and the model’s interpretation so the boundary is visible.
Missing information versus unsupported capability
Under-specified intent means the task could be completed if the user supplied a missing choice or value. Intent not supported means the deployment lacks a tool or permission to perform the requested operation. Asking for more detail cannot create a capability that is not available; the agent should state the boundary instead of pretending to act.
Guardrail versus system failure
A guardrail is an intentional safety, access or policy decision. A system failure is an unintended interruption such as an unavailable endpoint or connectivity error. Both can stop execution, but they require different responses: respect a guardrail, while diagnosing and safely retrying a system failure under an explicit policy.
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Worked diagnostic patterns
The agent acted despite a required review
Suppose a workflow requires a human review before a change is submitted. The trace shows a plan containing the review, followed by a tool call that submits the change without any review result. The earliest consequential divergence is the skipped checkpoint, so this is a plan adherence failure. Adding a general instruction such as “be careful” is weaker than enforcing a controller rule that submission requires a recorded review outcome.
The agent used a plausible but unsupported value
If a returned record contains no delivery date but the next response supplies one and schedules an action around it, inspect the model response immediately before the action. If no source provides the date, classify the incident as invention of new information. The correction is to require evidence for that field and to route missing values to a clarification step.
The tool returned a warning that was treated as success
A tool may return a structured response indicating that a request was accepted for processing but not completed, or that permissions limited the result. If the next action assumes completion, compare the exact status and message fields with the model’s interpretation. This is misinterpretation of tool output unless the endpoint itself reported an error inaccurately.
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When the user asks for an operation that no registered tool can perform, the correct trace outcome is a capability explanation or an alternative that stays within available tools. A fabricated confirmation is an intent-not-supported failure followed by invention. Adding another prompt cannot solve the missing capability; the system needs a tool, permission or a revised request.
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| Evidence in the run | Most relevant change | What to verify on re-run |
|---|---|---|
| The request was ambiguous or omitted a required value. | Add a clarification question and represent the unanswered field explicitly. | The agent pauses for the missing input instead of selecting a default. |
| The plan conflicts with the user’s goal or authorization. | Improve goal and constraint representation; add a pre-execution plan check. | The proposed steps match the request before any side effect. |
| The selected tool cannot perform the operation. | Revise tool descriptions, routing rules or capability declarations. | The chosen tool’s contract covers the requested operation. |
| Arguments violate the schema. | Add structured validation and clearer required-field definitions. | Malformed calls are rejected before dispatch and produce an actionable error. |
| The tool result is ambiguous or hard to parse. | Normalize the result, expose explicit status fields and include machine-readable error details. | The next model response quotes or uses the relevant returned value correctly. |
| The agent skips a planned check or exceeds a boundary. | Move the check into an enforceable execution policy rather than relying only on prose. | The action cannot proceed without the required checkpoint or authorization. |
| The endpoint fails or times out. | Implement bounded retries, clear failure states and safe recovery behavior. | A failed call is not mistaken for completed work, and retries do not duplicate side effects. |
| A handoff loses state or constraints. | Define an explicit handoff contract containing goal, constraints, state and expected output. | The receiving agent has the same authorization and relevant context. |
Evaluate both the incident and the pattern
A corrected incident should become a repeatable evaluation case. Save the original request, the relevant starting state, expected constraints, the failure label, the first divergent step and the evidence supporting that label. Then run the corrected system on the same case and verify both the final outcome and the path taken.
Single-case success is not enough. The OpenAI cookbook’s evaluation workflow recommends collecting traced runs, evaluating individual agents, handoffs, tools and completed runs, normalizing traces and looking for repeated patterns across the population. A plausible final result can conceal an ignored incentive, a missed stockout or a skipped review; recurrence analysis exposes those patterns.
Separate local findings from population-level findings:
- Agent-level: Does one agent repeatedly misunderstand a particular instruction or omit a checkpoint?
- Handoff-level: Do delegated tasks lose constraints, state or required output format?
- Tool-level: Are one tool’s arguments, schemas or result formats associated with invalid calls or misinterpretations?
- Run-level: Do failures cluster under long trajectories, retries, particular permissions or endpoint conditions?
The cookbook’s worked EV-order example uses synthetic traces with precomputed labels. Treat that setup as an illustration of an evaluation workflow, not as production evidence or a benchmark for another deployment.
What current evidence says about automated failure localization
Microsoft Research reports that AgentRx was evaluated on 115 manually annotated failed trajectories in 2026. In its own experiments, the project reports a 23.6 percentage-point absolute improvement in failure-localization accuracy and a 22.9 percentage-point improvement in root-cause attribution relative to prompting baselines. Those are project-reported results under that evaluation setup, not a guarantee that the same gains will occur in your system.
The practical implication is narrower and more useful: structured, evidence-backed localization is a better debugging target than asking an agent to explain its final answer after the fact. Any automated diagnosis should still be checked against the recorded request, tool arguments, results, permissions and system statuses.
Operational safeguards that prevent the next wrong step
- Require confirmation for irreversible actions. Keep drafting and execution separate when a side effect cannot be easily undone.
- Constrain tools by capability and authorization. A tool description should state what it does, required arguments, side effects and permission boundary.
- Make results machine-readable. Distinguish completed, partial, blocked, empty and failed outcomes instead of returning one ambiguous text field.
- Record delegated context. Log the handoff, the receiving agent’s inputs and the returned result in the same trace.
- Preserve raw evidence. Keep original tool results alongside normalized summaries so a later diagnosis can check whether interpretation changed the meaning.
- Stop safely on uncertainty. When required information is missing or a result conflicts with the plan, ask or halt rather than silently selecting a default.
- Test long trajectories. Include cases with retries, partial failures, handoffs and changing state; a short happy-path test will not expose plan drift.
A compact checklist for the next incident
- State the requested outcome, prohibitions, missing information and authorization limits.
- Open the complete trace for the affected turn, including delegated work.
- Read the user request, model response, tool name, arguments, raw result, next action and status fields in order.
- Mark the first consequential mismatch with intent, current state, evidence, plan or permissions.
- Assign one primary category from the taxonomy before proposing a fix.
- Change the smallest relevant component: clarification, plan, schema, result handling, policy, capability or reliability.
- Re-run the original case and confirm that the corrected path, not merely the final wording, is acceptable.
- Add the case to recurring-pattern evaluation and inspect similar traces for the same divergence.
Bottom line
When an AI agent takes the wrong next step, do not start by rewriting the prompt or blaming “reasoning.” Reconstruct the trajectory, locate the earliest consequential divergence, and distinguish intent, planning, tool-use, interpretation, execution, guardrail and system failures. A fix is trustworthy only when it succeeds on the original case and reduces the same failure pattern across later traced runs.
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