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When an AI agent chooses the wrong tool, sends bad arguments, routes work to the wrong agent, or repeats an unsafe action, do not start by rewriting the prompt. Capture the failed run, trace every decision and tool result, find the first point where behavior diverged from the intended path, then change the contract or control at that boundary and replay the case through a regression evaluation.
Start with the first wrong event, not the final answer
An agent’s final response is often the last symptom in a chain. A model may have selected an inappropriate tool, produced an argument that was syntactically valid but semantically wrong, received incomplete data, lost state during a handoff, or retried an action after a transient error. By the time the user sees the answer, several later steps may have hidden the original mistake.
The dependable debugging unit is a complete, timestamped trace of one failed run. The trace should show the input and relevant state, retrieved context, model configuration, tool definitions, selected tool, arguments, tool output, handoffs, retries, guardrail results, and final response. Compare that observed sequence with an explicit expected sequence and mark the earliest mismatch. Every later diagnosis should be based on evidence from that point.
“Debug one workflow run and understand what happened.”
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Preserve a safe, reproducible failing example
Save the smallest case that still produces the wrong action. Include enough context to replay it, but remove secrets and personal data before putting the trace in tickets, datasets, or shared dashboards. Traces can contain user messages, retrieved documents, credentials accidentally passed as arguments, and complete tool responses.
Record the run envelope
- Request: the exact user input, including relevant conversation turns and locale or time-zone settings.
- Agent state: session variables, memory, retrieved passages, feature flags, authentication claims, and any pending task or transaction identifier.
- Runtime configuration: model and SDK versions, temperature or other sampling settings, enabled tools, routing rules, and the system and developer instructions in effect.
- Actions: every model call, tool name, argument object, execution status, returned data, timeout, retry, and cancellation.
- Control decisions: handoffs, policy checks, input or output guardrails, authorization results, and human-approval decisions.
- Outcome: the user-visible response and any external side effect, such as a message sent, record changed, file deleted, or payment initiated.
Redact without destroying the bug
Replace tokens, passwords, API keys, private names, addresses, and regulated data with stable redaction labels so the same value remains recognizable across the trace. Preserve data types, lengths where they matter, ordering, and relationships between fields. A blanket deletion of the retrieved context or tool output can make the failure impossible to reproduce.
Write the expected behavior before editing anything
State what the agent should have done in operational terms: which tool or agent was appropriate, the required argument values and constraints, actions that were forbidden, and what outcome counts as acceptable. “Give a better answer” is too vague to grade. “Call lookup_order with the authenticated order ID, never expose another customer’s record, and ask for confirmation before cancellation” is testable.
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Trace the entire execution path
OpenAI’s Agents SDK observability guidance describes traces that can include model calls, tool calls and outputs, handoffs, guardrails, and custom spans. Use that same end-to-end view even if your agent uses another framework. A log of only the final model message cannot tell you whether the model, router, tool, or state layer caused the error.
| Trace segment | What to inspect | Questions to answer |
|---|---|---|
| Input and context | User request, conversation, retrieved documents, memory, identity and permissions | Did the agent receive the facts and authority needed for the task? Was stale or conflicting context present? |
| Model decision | Model version, instructions, available tools, selected action and stop reason | Was the intended capability available and distinguishable from similar tools? Did the model decide to act, ask, or hand off? |
| Arguments | Raw arguments, parsed types, defaults, validation errors and normalized values | Are values valid and semantically correct, or merely accepted by a permissive parser? |
| Execution | Authorization, side-effect status, latency, timeout, exception and external request ID | Did the requested operation actually run, partially run, or fail after changing state? |
| Tool output | Returned fields, freshness, confidence, pagination, error shape and truncation | Was the result complete and unambiguous? Did an error look like a successful empty result? |
| After-tool reasoning | State updates, next model call, retry decision, handoff and final response | Did the agent interpret the result correctly, preserve identity and constraints, and avoid repeating a side effect? |
Mark each event as expected or unexpected. The first unexpected event is the working fault boundary. If a later tool call is wrong but its input was already wrong, fix the argument-generation boundary rather than the tool implementation. If the tool returned the right data and the next decision was wrong, focus on result representation, state persistence, or instructions at that step.
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Classify the first divergence
The agent selected the wrong tool
Check the tool names, descriptions, examples, parameter schemas, and the set of capabilities exposed at that moment. Similar names, overlapping descriptions, or a broad “do anything” tool make selection ambiguous. A tool that can both preview and commit an operation is especially risky: the model may choose a destructive mode when a read-only operation was intended.
Confirm that the user request actually maps to the selected capability. If the request was underspecified, the correct action may have been a clarifying question. Do not label a clarification failure as a tool-selection failure until the trace shows that the required information was already available.
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Separate structural validity from semantic validity. JSON can conform to a schema while containing the wrong account, date range, currency, record identifier, or action mode. Validate enumerations, units, ownership, ranges, cross-field relationships, and authorization context before execution. Reject unknown fields and ambiguous defaults where the tool supports consequential actions.
Compare the arguments with the exact context shown to the model. A lost conversation turn, an incorrectly merged memory record, or a stale retrieval result can explain an apparently irrational value. Also check transformations between the model’s output and the function call: coercion, truncation, locale-specific date parsing, and automatic default insertion are frequent fault boundaries.
The tool returned stale, incomplete, or misleading data
Inspect the raw response, not only the formatted text shown to the model. Check timestamps, pagination, cache behavior, empty-result semantics, error fields, confidence indicators, and whether a timeout produced a partial object. Make failures explicit in the schema. Returning an empty list for both “no records” and “database unavailable” invites the agent to claim that nothing exists.
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When freshness matters, return a timestamp or version and enforce a freshness policy in application code. When a result is incomplete, require the agent to fetch the next page or ask the user rather than infer missing facts.
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Look at the exact result supplied to the next model call and the state written between calls. A serializer may omit a field, a summarizer may reverse a negation, or a handoff may drop the authenticated subject. Distinguish a model interpretation error from a representation error by comparing raw tool output, serialized context, and the model’s next decision.
The agent handed off, retried, or looped incorrectly
Inspect routing criteria, handoff payloads, retry limits, idempotency keys, and stop conditions. A specialist agent may receive a request without the constraints that the original agent had. A retry after an unknown timeout can duplicate a side effect unless the operation is idempotent or the system checks its status first. Repeated calls often indicate that a success condition was not written back to shared state.
A guardrail or policy decision changed the path
Record whether a guardrail blocked, modified, or replaced a call and whether the agent saw that decision. A blocked action followed by an unguarded fallback can look like a model failure when the real issue is inconsistent enforcement across tools.
Audit the capability contract
Treat every tool as a contract between the model and ordinary software. Review four layers together:
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- Description: Explain when to use the tool, when not to use it, side effects, required confirmation, and the meaning of each argument. Put distinctions between similar tools in the description rather than relying on hidden implementation knowledge.
- Schema: Use strict types, closed enumerations, required fields, sensible bounds, and cross-field validation. Model-visible descriptions should define units, time zones, identifier formats, and whether a value is a preview or a commit.
- Permissions: Enforce authorization in the tool service, not only in the prompt. The runtime identity, resource owner, and allowed operation should be checked for every call.
- Implementation: Verify that the function does exactly what its name and schema promise, returns machine-readable errors, and does not silently perform a stronger action than requested.
Expose the smallest capability set needed for the current task. Removing irrelevant tools reduces accidental matches and limits the impact of a bad decision. For high-consequence operations, split read, preview, and commit into separate tools and require an explicit confirmation or approval boundary before the commit call.
Put validation at the boundary that can still stop the harm
Controls are most effective immediately before a side effect and immediately after an external result enters the agent. Validate model-produced arguments before executing them, and validate consequential tool outputs before allowing the agent to act on them.
Input and output tool guardrails
In the supported local function-tool pipeline of the OpenAI Agents SDK for JavaScript, input tool guardrails can reject a call before execution, while output tool guardrails can reject or replace a result afterward. This coverage is specific to that execution path. The documentation states that hosted MCP tools, other hosted tools, and built-in execution tools do not use the same guardrail pipeline, so verify which path your agent actually invokes before depending on these hooks.
Regardless of framework, implement equivalent checks in the tool service for authorization, resource ownership, argument constraints, and side-effect policy. A model-level check is not a substitute for server-side access control.
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OpenAI’s practical guidance recommends multiple specialized guardrails combined with authentication, authorization, strict access controls, and standard software security measures. Useful layers include:
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- Input screening for prohibited requests, prompt injection indicators, and missing confirmation.
- Schema and semantic validation for every tool argument.
- Authorization against the current user and resource at execution time.
- Rate, spend, volume, and destination limits.
- Idempotency keys and transaction-status checks for retried operations.
- Human approval for irreversible, high-value, or externally visible actions.
- Output validation that prevents unsupported claims from being presented as completed work.
- Auditable logs with correlation IDs linking model calls to external side effects.
Turn the failure into a regression evaluation
Once the fault boundary is understood, convert the interaction into a test that can fail automatically. Include the original case and nearby cases that differ in the way a fix might accidentally break.
Define a grading rubric
- Action: The intended tool or handoff was selected, and prohibited actions were not attempted.
- Arguments: Required fields, ownership, ranges, units, and confirmation state are correct.
- Control behavior: The agent asks for clarification or approval when required and respects blocked operations.
- Outcome: The external state matches the requested, authorized result, or the agent accurately reports why it could not complete the task.
- Communication: The final response does not claim success when execution failed or data is uncertain.
Evaluate at three levels
LangChain’s evaluation guidance distinguishes single-step, full-turn, and multi-turn evaluation. A single-step test checks a tool choice or argument object in isolation. A full-turn test checks the complete request-to-response path. A multi-turn test checks memory, clarification, handoffs, and state changes across a conversation. Use the smallest level that catches the defect, then add the broader level when state or routing is involved.
OpenAI’s evaluation guidance recommends using trace grading to investigate individual runs and questions such as whether the right tool was chosen, whether a handoff was appropriate, and whether a prompt or routing change improved behavior. When you need repeatability, move from individual traces to a dataset and repeatable evaluation runs. Production traces are valuable sources of new cases, but redact them and review them before adding them to an offline set.
Change one implicated factor and replay
Do not simultaneously rewrite the prompt, rename tools, change the model, and alter retry logic. Make one targeted change tied to the trace: clarify a tool description, tighten a schema, preserve a missing state field, correct a router condition, or add a boundary check. Replay the exact failing case first, then run neighboring and adversarial cases.
Compare both behavior and side effects. A fix that produces the correct final text but still calls the wrong tool is not a fix. A guardrail that blocks the bad call but causes the agent to loop is only a partial fix. Record the changed version, rubric scores, blocked actions, latency, and any new failure modes. Keep the regression case permanently in the evaluation set.
Common symptoms and targeted fixes
| Symptom | Likely boundary to inspect first | Targeted remedy |
|---|---|---|
| Correct request, unrelated tool | Tool descriptions, overlapping names, exposed capability list | Clarify use and non-use cases; remove irrelevant tools; add a selection test. |
| Valid JSON, wrong customer or resource | Identity propagation, memory merge, authorization check | Bind resource ownership in server code and validate identifiers against the authenticated subject. |
| Destructive action when a preview was expected | Tool contract and confirmation boundary | Split preview and commit tools; require explicit confirmation and server-side enforcement. |
| Agent says “not found” during an outage | Error schema and empty-result handling | Use distinct error states for unavailable, unauthorized, empty, and partial results. |
| Duplicate external actions | Retry policy, timeout handling, idempotency | Use idempotency keys, query status after unknown outcomes, and cap retries. |
| Specialist ignores earlier constraints | Handoff payload and shared state | Pass the relevant request, identity, permissions, and prohibitions explicitly; test multi-turn handoffs. |
| Blocked call followed by unsafe fallback | Guardrail coverage across tool types | Verify the execution path and enforce the same policy in the tool service and fallback route. |
Instrument traces so the next incident is answerable
Give every run, model call, tool call, handoff, and external request a correlation identifier. Store timestamps, parent-child relationships, versions, and status transitions. A useful event record contains fields such as:
{
"run_id": "run-1842",
"event_id": "event-07",
"parent_id": "event-06",
"kind": "tool_call",
"tool": "lookup_order",
"arguments": {"order_id": "redacted-42"},
"authorized_subject": "user-redacted",
"status": "rejected",
"validation_errors": ["order does not belong to subject"],
"agent_version": "agent-2026-10-17",
"trace_time": "2026-10-17T14:22:31Z"
}Keep raw and redacted views separate, restrict access to sensitive traces, define retention periods, and make export auditable. The goal is observability of events and inputs or outputs that your system recorded—not access to private internal reasoning. You can diagnose a workflow from observable decisions, arguments, results, and state transitions without claiming to see hidden chain-of-thought.
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A repeatable incident checklist
- Freeze and redact the failing trace; record versions and configuration.
- Write the expected tool, arguments, controls, and outcome.
- Inspect input, context, model choice, arguments, execution, output, handoff, retry, and final response in order.
- Mark the earliest divergence and classify it as selection, arguments, execution, result, state, routing, or control.
- Verify the tool description, strict schema, implementation, permissions, and runtime exposure at that boundary.
- Add or correct validation where the unwanted side effect can still be stopped.
- Convert the incident into a graded regression case with neighboring cases.
- Change one implicated factor, replay the original case, then run the broader evaluation set.
- Review side effects, not just text quality, and retain the test for future releases.
This process replaces guesswork with a chain of evidence. The trace tells you what happened, the contract review explains why the action was possible, the boundary control limits damage, and the evaluation proves whether the change generalizes.
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