The Tool Desk
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This guide presents a provider-neutral workflow, with concrete behavior from OpenAI’s agent documentation where it is documented. Endpoint fields, authentication rules, status codes, idempotency guarantees, and retry instructions must still be checked in the unfamiliar API’s current official reference.
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What “stalled” means in an agent run
An agent run is not one request. It is a loop that may contain several model generations, tool executions, and handoffs. The runner continues until it reaches a stopping point such as a final response, an error, a deadline, or a configured turn limit. A quiet or incomplete final answer therefore does not identify the failing component.
Typical symptoms map to different causes:
- It waits indefinitely: an HTTP client, proxy, tool wrapper, model attempt, or orchestration loop has no effective deadline.
- It repeats the same action: the model is receiving an unhelpful error, the tool result is not being returned, or retries are replaying a non-transient failure.
- It stops with partial work: a later tool failed after earlier tools changed external state, or the run hit a turn/deadline limit.
- It returns confidently wrong work: a tool error was converted into a success-shaped result, so the model never learned that the operation failed.
The first diagnostic question is therefore: which span was the last completed one, and which span is slow, failed, or still active?
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Build a timeline before changing prompts or code
Reproduce the issue with a stable run identifier and record enough context to compare attempts. At minimum, save:
- Run ID and a timestamp with timezone.
- Agent and framework versions, model name, and relevant configuration.
- Target API, endpoint, API version, and whether the request was streaming.
- Whether the run used one agent or handed work to another.
- Client, proxy, and execution-environment versions.
In the OpenAI Agents tracing model, a session contains turns, and turns contain spans for model generations and tools. Spans expose duration, status, recorded data, and error information. Use that hierarchy to locate the first abnormal span rather than inferring a cause from the final text.
- Open the run timeline or trace.
- Find the last span that completed successfully.
- Identify the first span that is unusually slow, failed, or remains active.
- Check whether later spans are merely waiting on that span.
- Record the exact transition: for example, “model emitted tool call; HTTP request started; no response after 20 seconds.”
A streaming run needs an additional check: determine whether tokens stopped arriving while the connection remained open, or whether the stream ended cleanly and the application failed to assemble the result. These are different failures.
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A trace is useful only when it explains what crossed each boundary. For every model generation, record the input and output messages or a privacy-safe representation, start and end times, duration, status, and error details. For every tool call, record the tool name, sanitized arguments, request method and endpoint, relevant non-secret headers, response status, response body shape, elapsed time, and correlation or request ID when the provider supplies one.
Redact API keys, authorization headers, cookies, personal data, payment data, and customer payloads before exporting or sharing traces. Keep a consistent redaction policy so that the same field is not exposed in one span and hidden in another.
Verify the complete return path:
- The model emitted the tool name and arguments you expected.
- Your wrapper sent the intended request, rather than silently dropping or renaming fields.
- The wrapper captured the response or exception.
- The wrapper returned a result to the model, including an explicit failure state when the operation failed.
- The orchestrator associated the result with the correct tool call and continued the intended turn.
OpenAI’s tracing documentation describes exporting session traces as OTLP JSON for inspection elsewhere. Export is useful when the built-in view is not enough, but it does not replace provider-specific logs, gateway logs, or the API’s request identifier.
Inspect the tool boundary first
The boundary between a model-generated tool call and your API client is where many “agent” bugs become ordinary integration bugs. Compare the model’s emitted call with the wrapper’s actual request instead of trusting a high-level log.
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Compare the generated call with the wire request
- Tool name: confirm that the name maps to the intended function or endpoint.
- Arguments: check spelling, nesting, types, null handling, encoding, and omitted optional values.
- Authentication: verify which credential and project or account the wrapper actually selected.
- URL and method: confirm host, path, API version, HTTP method, and query parameters.
- Body: inspect the serialized JSON or form payload, content type, and payload size.
- Response handling: preserve status, headers that matter for diagnosis, body, and elapsed time.
Make failures legible to the model
Do not turn every exception into a result such as {"ok": true} or an empty list. Return a structured failure that distinguishes a rejected request from an unavailable service. Include a short, non-secret message, the HTTP status or provider error code when available, and whether retrying is allowed. The model can then correct an argument, ask for missing information, or stop instead of repeating the same call.
OpenAI’s Agents API error guidance states: “Inspect tool results even when a turn completes.” A completed turn can still contain a failed tool result that explains the incomplete work.
Classify the failure before selecting a fix
Different layers can produce similar symptoms. Use the response and the trace to classify the failure first.
| Diagnosis | Evidence to collect | Appropriate response | Do not do |
|---|---|---|---|
| Invalid request or schema | 4xx response, parameter pointer, validation message, serialized payload | Correct required fields, names, types, format, encoding, or size using the endpoint reference; reproduce the request independently | Replay the unchanged payload |
| Authentication or permission | Credential source, project/resource, token status, authorization response | Use an active credential with the required scope or role and verify the target resource | Retry unchanged credentials |
| Not found or incompatible resource | Endpoint, resource/model identifier, API version, environment and region | Check that the identifier exists and is accessible in this environment | Assume a typo is transient |
| Rate limit | 429 response, limit headers, provider message, Retry-After |
Honor the supplied delay; otherwise use bounded exponential backoff with jitter and account for SDK retries | Launch immediate parallel retries |
| Timeout or connection failure | Which layer timed out, elapsed time, socket/proxy logs, provider request ID | Set a deadline for that layer, inspect network path, and retry only when replay is safe | Assume the provider received nothing or everything |
| Server or environment failure | 5xx response, provider status, package/runtime and executor logs | Check service health and environment connectivity, then retry within a budget if the operation is safe | Keep replaying while the environment is broken |
Use a request client such as curl or Postman to send the same sanitized request outside the agent. If it fails there too, focus on the API contract, credentials, network, or service. If it succeeds independently, compare serialization, headers, timeout settings, and response parsing in the agent wrapper.
Put an independent deadline around every layer
A single “agent timeout” is rarely sufficient. Set separate bounds for the model, each external tool, and the overall run.
Model-call timeout
In the OpenAI Agents SDK, ModelSettings.timeout limits one model-call attempt. It does not limit the complete agent run, function-tool execution, or retry backoff. A model call can therefore time out while the run remains active, or the model can finish while a tool continues waiting.
Tool and HTTP timeout
Configure a timeout in the function tool or HTTP client for every external operation. Include connection, DNS, TLS, and response-read phases where the client separates them. A function tool can be configured to return an error to the model or raise an exception; choose deliberately. Returning a structured error lets the model decide whether to correct input, while raising an exception may be preferable when the orchestration layer should abort immediately.
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Run deadline and loop bound
Set an absolute run deadline and a maximum number of turns or tool calls. A deadline prevents a sequence of individually successful calls from consuming unlimited time. A loop bound catches a model that keeps issuing equivalent calls because progress is not visible.
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run_deadline = monotonic_now() + RUN_BUDGET
for turn in range(MAX_TURNS):
if monotonic_now() >= run_deadline:
return failure("run deadline exceeded")
result = run_one_model_attempt(timeout=MODEL_TIMEOUT)
if result.is_final:
return result
tool_result = execute_tool(timeout=TOOL_TIMEOUT)
deliver_tool_result(tool_result)
return failure("turn limit exceeded")The names in this example are illustrative. Use the equivalent settings in your framework, and ensure that lower-level timeouts cannot silently exceed the run deadline.
Retry only failures that can plausibly clear
Retries are a control mechanism, not a substitute for diagnosis. Retry a transient network interruption, a provider-declared overload, or a rate limit after its prescribed delay. Do not retry an invalid schema, missing permission, unknown resource, or unchanged credential.
Use provider timing instructions
When a response includes Retry-After, follow it unless your overall deadline expires first. If no valid delay is supplied, use bounded exponential backoff with jitter. Account for retries already performed by the SDK; otherwise an application-level policy can multiply the actual attempts.
Every retry policy needs both an attempt cap and a time budget. Stop when the error signature changes, the result becomes ambiguous, or the budget expires. Log the attempt number, selected delay, and reason without logging secrets.
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A timeout or disconnect does not prove that the provider or tool did nothing. Before resubmitting a create, charge, delete, publish, or update operation, determine whether the earlier request may have completed. Use an idempotency key or a provider-supported status lookup when available. If the API offers neither, query for the expected result or require human confirmation before replaying a potentially destructive action.
Use provider health data correctly
Compare the incident timestamp and endpoint with the target provider’s service-health information. A provider-wide outage supports an environment or service diagnosis, but a clean status page does not prove your client is healthy. OpenAI’s API latency troubleshooting guidance says: “Requests likely did not reach OpenAI” when client-side errors are absent from Service Health data. That pattern points you toward DNS, TLS, firewall, proxy, SDK, local resource exhaustion, or a client-side timeout path.
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Capture evidence from both sides when possible:
- Client start and end timestamps, exception type, and elapsed time.
- Proxy or gateway access logs, including upstream connection outcome.
- Provider request or correlation ID and response status.
- DNS, TLS, socket, and connection-pool errors.
- Service-health state for the same region and time window.
Reproduce unfamiliar APIs outside the agent
Agents add planning and interpretation around an API call. Remove that complexity temporarily. Build the smallest independent request that uses the same endpoint, method, headers, body, and credential scope. Run it with a command-line client or API testing tool, then compare the raw response with what the wrapper delivered to the model.
What an independent reproduction can prove
- It fails independently: investigate the API contract, credential, resource, network, rate limit, or service.
- It succeeds independently but fails in the agent: inspect tool schema, serialization, headers, timeout, retries, and response parsing.
- It succeeds but the agent repeats: inspect whether the tool result reaches the correct turn and whether failure or completion is explicit.
- It is ambiguous after a timeout: query operation status or inspect side effects before retrying.
Keep the reproduction sanitized and deterministic. Use a test resource where possible, and note the API version and date of the reference used. Unfamiliar APIs change field names, pagination, authentication schemes, and retry behavior; do not infer those details from another provider.
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Check the orchestration loop for false progress
Once the tool itself is healthy, inspect the runner. A loop can appear stalled when it is actually making progress that is invisible in logs.
- Log every transition from model generation to tool execution and back.
- Assign a unique call ID and parent span ID to each tool invocation.
- Record whether the model received a success, failure, or empty result.
- Detect repeated tool name plus normalized-argument pairs within one run.
- Stop or ask for clarification after a configured number of equivalent calls.
- Ensure handoffs identify the receiving agent and preserve the relevant context.
- Persist partial results so a later failure does not erase completed work.
Normalize arguments before repetition detection: ordering differences in JSON keys should not hide identical requests, while genuinely different pagination cursors should count as progress. A repetition guard should report the repeated call to the model or operator rather than silently dropping it.
Design error results that support recovery
A useful tool result has a stable shape. One practical pattern is:
{
"ok": false,
"error": {
"category": "rate_limit",
"message": "Provider requested a retry after 8 seconds",
"retryable": true,
"retry_after_seconds": 8,
"request_id": "redacted-or-safe-id"
}
}For a validation failure, include the parameter location and a correction hint. For an authorization failure, state that credentials or permissions must change. For an ambiguous timeout, mark the operation state as unknown and require a status check before replay. Keep messages short enough for the model’s context, while retaining full details in the trace.
A worked diagnostic sequence
Symptom: the agent repeats a create call
- The trace shows a model generation emitting the same tool and normalized arguments three times.
- The first tool span has a client timeout and no response body, so completion is unknown.
- The wrapper returned a generic empty object instead of an error state.
- An independent status lookup finds the resource was created by the first attempt.
- The tool is changed to return an “unknown outcome” result after a disconnect and to query status before retrying.
- A repetition guard stops equivalent create calls and asks the model to verify the resource.
The important finding is not that the model “got confused.” The missing outcome state and unsafe replay made progress impossible to observe.
Symptom: the agent waits while the provider shows no incident
- The trace shows no provider request ID and a socket timeout at the proxy.
- The application’s model timeout is five minutes, so the run remains blocked far longer than intended.
- Proxy logs show connection establishment failed before an upstream request was recorded.
- A tool-level connection timeout and run deadline are added; the network path is fixed separately.
Because the request never reached the provider, changing the prompt or increasing model retries would not address the cause.
Privacy, retention, and operational trade-offs
More trace data improves diagnosis but increases exposure and storage cost. Decide what to retain for each environment:
- Development: detailed arguments and results with synthetic data, short retention, and local access.
- Staging: production-like schemas with masked payloads, correlation IDs, and longer retention for regression analysis.
- Production: least-privilege access, field-level redaction, encrypted storage, explicit retention, and separate handling for customer content.
When evaluating an observability setup, compare whether it captures model and tool spans, arguments and results, errors and durations, timeline views, OTLP export, privacy controls, retention, framework coverage, provider coverage, and operational overhead. Official OpenAI documentation describes tracing and export capabilities, but it does not establish a neutral ranking of third-party products.
Provider-specific checks you must not generalize
The diagnostic method transfers across providers; implementation details do not. For the target API, verify:
- Exact authentication header, token audience, project or workspace scope, and permission model.
- Current endpoint paths, API versions, required fields, content types, and maximum payload sizes.
- Status-code meanings and structured error fields.
- Whether
Retry-Afteris seconds, a date, or omitted. - SDK default retries, timeout values, connection pooling, and streaming behavior.
- Idempotency-key support and how to query an operation whose outcome is unknown.
- Regional availability, quotas, pagination rules, and asynchronous-operation status endpoints.
Do not assume that a 429, timeout, or request ID has identical semantics across APIs. The target provider’s current reference is authoritative for those details.
Turn the workflow into a runbook
- Reproduce: record run ID, timestamp with timezone, versions, endpoint, streaming mode, and environment.
- Locate: find the last completed span and the first slow, failed, or active span.
- Inspect: compare model arguments with the actual sanitized wire request and response.
- Classify: label the failure as validation, authentication, resource, rate limit, timeout, connection, server, or environment.
- Reproduce independently: send the same request outside the agent.
- Bound: apply model-attempt, tool, and whole-run deadlines plus a turn limit.
- Retry safely: honor provider delays, cap attempts, use jitter, and verify side effects before replay.
- Repair the return path: deliver explicit success or failure results to the model and preserve request IDs.
- Verify: rerun with tracing enabled and confirm that the original failing span now completes or fails fast with a useful diagnosis.
This sequence keeps the investigation anchored to observable state. It also prevents a common mistake: treating a final answer as evidence that every preceding tool operation succeeded.
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