Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsStart with a trace of one failed run, not a new prompt. Follow the run from user input and context to model decision, tool arguments, execution result, handoffs, retries, guardrails and final response. The first point where observed behavior diverges from the intended behavior identifies the boundary to fix. Then change one relevant contract or control, replay the failure and test neighboring cases in a regression set.
The reliable debugging method
An agent can appear to give a bad answer when the actual defect happened much earlier: it may have selected the wrong tool, supplied a valid-looking but unsafe argument, received incomplete data, lost state during a handoff, retried an action, or been allowed to execute without a required approval. Debugging from the final response alone hides that chain.
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Use this sequence:
- Preserve a representative failed interaction and the expected outcome.
- Record the complete execution trace, including decisions and external effects.
- Locate the earliest unexpected event.
- Classify the defect at that boundary.
- Check the tool contract, context, state and permissions that produced it.
- Add validation or a guardrail at the boundary where the error can be stopped.
- Replay the original case and a broader evaluation set before rollout.
Tracing shows observable events and recorded inputs and outputs; it does not expose private internal reasoning. OpenAI’s observability guidance describes traces containing model calls, tool calls and outputs, handoffs, guardrails and custom spans, and presents a trace as a way to debug one workflow run and understand what happened.
1. Preserve a failing run before changing anything
Save the complete input and configuration
Copy the exact user message, conversation history, retrieved documents, user or account state, system and developer instructions, model and configuration identifiers, tool definitions, routing rules and feature flags. A timestamp and deployment version matter because the same prompt can behave differently after a model, schema or routing change.
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Write down the intended action in operational terms. “Help the customer” is not a testable expectation; “look up the order, verify that it is eligible for cancellation, ask for confirmation, then call the cancellation tool once” is.
Redact before exporting or sharing
Traces can contain tool inputs, outputs, messages and personal data. Remove credentials, access tokens, payment details, private documents and unnecessary identifiers before sending a trace to another person or system. Preserve enough structure to reproduce the decision: replace a real account number with a stable token rather than deleting the entire field.
Record side effects, not only text
Note whether an email was sent, a record changed, a payment attempted, a ticket opened or another irreversible action occurred. If execution has already caused harm, stop further retries and revoke or narrow the agent’s credentials before continuing diagnosis.
2. Trace the whole run
Open the run as a timeline. Do not inspect only the last assistant message. For each event, capture the data below.
| Trace point | What to inspect | Question it answers |
|---|---|---|
| Input and context | User request, history, retrieved content, state and active instructions | Did the agent receive the information required to choose correctly? |
| Model call | Model/configuration, available tools, selected action or response | Was the decision possible and consistent with the presented context? |
| Tool call | Exact tool name and serialized arguments | Was the right capability selected, with the right values? |
| Tool result | HTTP or function status, returned fields, errors, freshness and latency | Did execution return what the agent was told to expect? |
| Handoff or routing | Source agent, destination agent, reason and transferred state | Was work sent to the right specialist without losing context? |
| Guardrail and retry | Checks, rejection messages, retry count and altered arguments | Did a control stop, modify or accidentally repeat the action? |
| Final result | Visible answer and all external side effects | Did the outcome satisfy the stated contract? |
Use correlation IDs to connect model calls, tool executions and downstream service logs. A trace should make it possible to answer “what happened next?” without reconstructing events from memory.
3. Find the first divergence
Write the expected timeline
Before judging the trace, list the intended steps and forbidden actions. For a refund request, an expectation might be: identify the order, verify eligibility, calculate the amount, ask for confirmation, call the refund function once, and report the result. Calling a shipping tool, refunding before confirmation, or using a different amount is a distinct failure, even if the final prose sounds plausible.
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Compare that timeline with the trace from the beginning. The first divergence is usually more useful than the last visible symptom. If the agent selected a shipping tool when it should have selected an order lookup, fixing the final wording will not solve the cause.
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Classify the defect at its starting point
| Observed first error | Likely boundary to inspect | Useful evidence |
|---|---|---|
| Wrong tool | Tool descriptions, names, routing instructions, available-tool set and permissions | What tools were exposed and how their purposes differed |
| Right tool, wrong arguments | Schema, required fields, types, examples, normalization and state mapping | Model-produced arguments versus validated arguments |
| Tool returns bad or surprising data | Implementation, upstream service, timeout handling and freshness | Raw response, status, error and timestamp |
| Correct result misread | Output schema, error representation, prompt and state update | Returned payload and the next model input |
| Lost or stale state | Handoff serialization, memory writes, retrieval and concurrency | State before and after each transition |
| Repeated or unsafe action | Retry policy, idempotency, confirmation and authorization | Attempt count, request IDs and side-effect logs |
These are hypotheses, not labels to apply without evidence. The trace determines which one occurred.
4. Check the capability contract
Make tool purposes unambiguous
Read the exact name and description supplied at the failing model call. Descriptions should state what a tool does, when to use it, when not to use it, required preconditions and the meaning of each field. Avoid overlapping tools whose names differ only by an abbreviation. If two tools can both “update a customer,” explain which one handles profile data and which one handles billing, and expose only the one needed for a constrained task.
Compare the advertised behavior with the implementation. A description that promises a read-only lookup while the function also mutates data creates an unsafe contract even when the model follows the description.
Validate the schema and semantics
Types catch a string where an integer is required, but they do not catch a semantically wrong order ID, a date in the wrong timezone or a refund amount copied from an unrelated message. Validate required fields, allowed values, ranges, ownership and relationships before execution. Normalize formats in one place and reject ambiguous values instead of silently guessing.
Log the arguments after serialization and after server-side validation. The two versions reveal whether corruption occurred in model output, argument parsing or application mapping. Never treat a schema-valid call as automatically safe.
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Limit available capabilities
At each step, expose the smallest set of tools and permissions that can complete the task. Authentication establishes identity; authorization must still restrict which records and operations that identity can access. Separate read and write credentials where possible, and require explicit confirmation or a second policy check for consequential actions.
5. Inspect context, state and handoffs
Check what the model actually saw
Inspect the rendered prompt or request sent at the failing step, not the template stored in source control. Verify that retrieved content was present, ordered correctly and labeled with its source and freshness. Look for truncation, conflicting instructions, stale memory, duplicated messages and a missing tool result.
If the correct fact exists only in application state, confirm that the state was serialized into the next call. Do not assume that an earlier agent’s memory is automatically available to a later one.
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For every handoff, record why the source agent transferred work, which destination was selected and exactly what context crossed the boundary. A specialist may receive the user’s last sentence but not the authorization status, verified account, tool result or required constraints. Make those fields explicit in the handoff payload and validate them on receipt.
Separate retries from new decisions
A retry can be appropriate for a timeout but dangerous for a completed write whose response was lost. Classify errors as transient, permanent or unknown, and use idempotency keys for operations that can change data. The trace should show whether a retry reused the same arguments, changed them, or triggered an entirely new tool choice.
6. Verify tool execution and output handling
Inspect the raw result returned to the agent. A successful HTTP status may still contain an application-level failure, partial data or a stale cache. Check status codes, error fields, pagination, timestamps, units and null handling. If the tool returned an error, verify that the error was represented in a way the agent was instructed to understand.
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Then inspect the next model input. Did it receive the complete result, or only a summary that dropped a critical field? Did the application append a second instruction that changed the task? If the result is authoritative, mark it as such and preserve its provenance; if it is advisory or stale, make that visible.
For consequential outputs, validate again after the model responds. Confirm that an amount, recipient, record identifier or status in the proposed action matches the trusted state. This post-decision check is independent of whether the model’s prose appears confident.
7. Add controls at the right boundary
Put a check where the failure can be stopped, rather than adding a broad instruction at the end of the workflow. A tool-selection problem needs routing or capability controls; a bad amount needs argument and authorization validation; a dangerous result needs an output or approval gate.
Understand function-tool guardrails
In the supported local function-tool pipeline of the OpenAI Agents SDK for JavaScript, input tool guardrails can reject a call before the function executes, and output tool guardrails can reject or replace a result afterward. That coverage is not universal: the SDK documentation distinguishes local function tools from hosted MCP tools, other hosted tools and built-in execution tools, which do not use the same guardrail pipeline. Verify the execution path before relying on a particular hook.
| Control | Best use | Typical failure it prevents |
|---|---|---|
| Input validation | Reject malformed, unauthorized or out-of-scope arguments | Wrong record, amount, recipient or operation |
| Output validation | Reject incomplete, inconsistent or unsafe tool results | Agent acting on stale or partial data |
| Human confirmation | Pause before irreversible or high-impact actions | Unintended writes, messages or payments |
| Least-privilege authorization | Restrict data and operations independently of model choice | Valid request applied to an unauthorized target |
| Rate and retry limits | Bound repeated attempts and runaway loops | Duplicate side effects or resource exhaustion |
Layer these controls with authentication, authorization, strict access policies and ordinary software-security practices. A single prompt rule is not a substitute for enforcement in the application or service that owns the data.
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8. Turn the failure into an evaluation case
Define a rubric that can be scored
For each preserved failure, record:
- The intended tool or non-tool response.
- Required and forbidden arguments, including semantic constraints.
- Required confirmation, authorization and ordering conditions.
- Acceptable tool results and the permitted final outcome.
- Whether a handoff, retry or refusal is expected.
A grader should be able to mark the trace without guessing what “good” means. Include negative cases, such as a similar request that must not trigger the write tool.
Use the right evaluation level
| Level | What it tests | When to use it |
|---|---|---|
| Single-step | Tool choice, argument validity or one handoff | After changing a description, schema or router |
| Full-turn | One request from input through final response and side effects | When ordering, confirmation or output interpretation matters |
| Multi-turn | State carried across several messages or agents | When memory, clarification and handoffs are involved |
Trace grading is useful while investigating a small number of runs: ask whether the right tool was chosen, whether a handoff was appropriate, whether policy was followed and whether a prompt or routing change improved the result. Once the expected behavior is clear, move those cases into a dataset and run them repeatedly. Production traces often reveal additional failures that should become offline test cases.
9. Change one factor and replay
Make the smallest change supported by the trace: clarify one tool description, tighten one schema, fix one state field, alter one routing rule or add one boundary check. Keep the original failing trace as a baseline. Replay it with the same input, context and configuration wherever possible, then run neighboring examples that exercise similar tools and arguments.
Compare more than the final text. Check first tool choice, argument validity, tool results, handoffs, retries, guardrail decisions, latency and side effects. A fix that solves one example while causing valid requests to be refused is not ready. Expand the evaluation set when the rubric exposes a new edge case, and record the model, SDK and application versions with every run.
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Common failure patterns and targeted fixes
The agent chooses a plausible but unrelated tool
Inspect the available-tool list and descriptions at that call. Remove unnecessary tools, distinguish overlapping names, add explicit “do not use when” conditions and test near-miss requests. If routing is deterministic for a class of operations, perform that routing outside the model and let the agent handle only the variable portion.
The tool is correct but the target is wrong
Compare the user’s identifier with the serialized argument and the server-resolved record. Require ownership and existence checks in the service, not only in the prompt. Reject ambiguous names and ask for clarification before a write.
The agent acts on an error as if it were success
Inspect how failures are encoded and whether the next model call receives status, error details and retry guidance. Use a typed result with explicit success and failure states, then add an output check that blocks a write when required data is missing.
The same action happens twice
Check whether the first request completed before its response was lost. Add idempotency keys, bounded retries and a reconciliation lookup before repeating a side effect. A timeout is not proof that the operation did not happen.
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Compare state before and after transfer. Make authorization, confirmation, identity and relevant tool results required handoff fields, and reject a destination request that lacks them rather than letting the specialist infer them.
Release checklist
- A redacted trace contains the full failing sequence and external effects.
- The first divergence is identified and tied to a specific boundary.
- Tool names, descriptions, schemas, implementations and permissions agree.
- Arguments and consequential outputs are validated semantically, not only by type.
- Handoffs preserve required state, and retries are bounded and idempotent.
- Guardrail coverage matches the actual tool execution path.
- Authentication, authorization and least-privilege controls operate outside the model.
- The failure is in a scored regression case with positive and negative neighbors.
- The proposed change improves the original case without regressing the representative set.
- Trace retention and redaction rules are appropriate for the data being captured.
Bottom line
Wrong actions are workflow defects, not merely wording defects. A complete trace turns a vague complaint into a precise question: where did the observed run first leave the expected path? Fix that boundary, enforce the contract in software, and prove the change with repeatable evaluations. This approach scales from a single incorrect tool call to failures involving state, handoffs, retries and safety policy.
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