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An agent that returns an incomplete answer without raising an exception is not necessarily a mystery in the model. The missing clue may be a tool call that never ran, a handoff that was not recorded, a guardrail that stopped the run, an export that never finished, or instrumentation that covers only part of the workflow. A useful trace harness makes those possibilities visible by attaching one trace to each logical run, recording nested spans for the work inside it, and preserving status, timing, and policy-approved details for inspection.
This guide shows how to build and use that harness without assuming that every framework records the same fields. The goal is not merely to collect telemetry. It is to determine whether the defect is in model generation, tool execution, agent routing, policy enforcement, runtime behavior, or the observability layer itself.
What a “quiet failure” actually is
An explicit exception is comparatively easy to route to an error handler. A quiet failure reaches the application boundary looking successful or inconclusive while the expected work did not happen. Typical examples include:
- A run completes with a short or empty answer even though a retrieval or calculation step was expected.
- The agent says it could not perform an action, but no tool error appears in application logs.
- A handoff to a specialist agent seems to have been skipped.
- The user receives a plausible answer that omits one required operation.
- The run has no visible diagnostic record, so you cannot tell whether the agent, a tool, or the tracing system failed.
These are symptoms, not a measured failure category. There is no reliable prevalence figure to attach to them. Treat the absence of an exception as “the boundary did not report an exception,” not as proof that every internal step succeeded.
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Trace, span, and the shape of an agent run
A trace is the run-level container for one logical request or task. A span is a timed operation inside that trace. Spans can be nested, so a run can show which operation called another operation, how long each took, and which one first reported an error or unexpected status.
The following is an illustrative, framework-neutral shape:
trace: customer-question-8f31
└── agent turn
├── model generation
├── function: search_catalog
│ └── HTTP request
├── guardrail check
└── handoff: billing_agentOpenAI’s Agents SDK documents task, agent, turn, generation, function, guardrail, and handoff span categories. Other frameworks may use different names or hierarchies, so regard the diagram as a debugging model rather than a promise about a particular SDK.
At minimum, each span needs a parent relationship, a start time, an end time or duration, and a status. Input and output fields are valuable when policy permits them, but they are not guaranteed to be recorded by every backend or configuration.
Design the minimum useful trace harness
Start at the boundary where your application accepts a request. Generate or accept a correlation identifier and create one trace for the logical run. Pass that identifier through every agent turn, model call, tool invocation, handoff, guardrail, and custom operation. A trace that begins only around the final model call cannot explain why earlier work was omitted.
Fields worth preserving
| Field | Purpose | Policy note |
|---|---|---|
| Trace ID | Connects all operations belonging to one run. | Usually safe to expose as an opaque identifier. |
| Parent and span IDs | Reconstructs nesting and identifies the caller of a failed operation. | Do not put user content in identifiers. |
| Operation type and name | Distinguishes generation, tool, handoff, guardrail, and custom work. | Use stable names so searches remain useful after deployments. |
| Start and end time, or duration | Reveals timeouts, unusually slow steps, and work that never started. | Use a consistent clock and timezone convention. |
| Status | Separates success, failure, cancellation, and unknown outcomes. | Define how your runtime maps exceptions and cancellations. |
| Safe context | Identifies the tenant, workflow version, tool name, or input class under investigation. | Use redaction, hashing, or classification instead of unrestricted payload capture. |
| Recorded input and output | Lets an operator compare what a step received with what it produced. | Capture only when organizational policy allows it. |
OpenAI’s Agents API tracing documentation describes a view that can show each step’s recorded inputs, outputs, duration, and status, with error details available for failed spans. That wording matters: “recorded” does not mean that every deployment automatically stores every payload.
Instrument the logical boundaries
- Create the trace. At request intake, assign a trace ID and attach workflow, deployment, and tenant context that is safe to retain.
- Wrap each agent turn. Record when the turn starts and ends, the selected agent or policy version, and its status.
- Wrap model generation. Record the model operation as a child span. If content capture is allowed, associate the relevant prompt and response with that span rather than with unrelated parent metadata.
- Wrap every tool call. Create a span before dispatch, propagate context into the tool or HTTP request, and finish the span on success, timeout, cancellation, or exception.
- Record handoffs and guardrails. A handoff span should identify the source and destination agent. A guardrail span should show whether the check allowed, blocked, or altered the operation.
- Add custom spans for opaque work. Retrieval, parsing, queueing, database access, and business-rule evaluation often determine the final answer but are not automatically visible.
- Finish and export. Close the trace after the final operation and invoke the framework’s documented flush or export lifecycle. A trace that exists only in an in-memory buffer is not available to an investigator.
Illustrative event data might look like this:
{
"trace_id": "8f31...",
"span_id": "a12c...",
"parent_id": "root...",
"kind": "tool",
"name": "search_catalog",
"start": "2026-10-18T12:00:01Z",
"duration_ms": 842,
"status": "error",
"attributes": {"workflow_version": "2026.10", "result_class": "timeout"}
}This is a schema example, not a drop-in API command. Use the field names and export mechanism provided by your framework.
Read a trace from the outside in
Debugging is faster when you first establish that the record is trustworthy, then inspect the work inside it.
- Confirm tracing is enabled. Check the runtime, environment, SDK configuration, and per-run options before interpreting an empty trace as an agent failure.
- Confirm completion and export. Verify that the trace was finished or flushed and that it is visible in the expected dashboard or export endpoint. The OpenAI Agents SDK documents a buffered export lifecycle; the Agents API documentation describes dashboard inspection and session-trace export through its API.
- Open the run-level record. Note its overall status, start time, end time, and duration. A successful parent status does not identify which child produced an incomplete result.
- Find the first anomaly. Scan in execution order for the first missing span, failed span, cancellation, or unexpectedly long duration. Later errors may be consequences of that earlier event.
- Compare parent and child operations. Check what the model generation requested, what the tool received, what it returned, and whether a handoff or guardrail changed the path. This span-level comparison is often more informative than the final run status.
- Check coverage. If the trace exists but an expected operation is absent, inspect instrumentation registration, content-recording settings, and semantic-convention configuration before blaming the model.
Use the first missing or failed span to classify the problem
| Trace pattern | Most useful next question | Likely investigation area |
|---|---|---|
| No trace at all | Was tracing enabled, and did export complete? | Runtime defaults, environment flags, buffering, credentials, or policy restrictions. |
| Trace exists with only a root span | Were child operations instrumented? | Framework coverage, registration, semantic conventions, or custom code without spans. |
| Generation span exists; tool span is absent | Did the model request a tool, and was dispatch reached? | Tool-routing logic, handoff or guardrail decisions, or missing tool instrumentation. |
| Tool span is present and failed | What input, timeout, response, or exception did the tool record? | Tool implementation, network, credentials, rate limits, or validation. |
| Handoff span is absent or ends early | Did the source agent select the handoff and did policy allow it? | Routing rules, guardrails, cancellation, or uninstrumented transition code. |
| All spans report success but output is wrong | Which recorded output first diverged from the expected contract? | Prompt or model behavior, schema validation, business logic, or an unrecorded transformation. |
The table is a decision aid, not a claim that one trace pattern proves one root cause. Use the recorded evidence to narrow the branch, then reproduce with a controlled input when necessary.
Runtime defaults and policy settings can hide the evidence
OpenAI’s Agents SDK documentation states that tracing is enabled by default in the documented server runtimes, but disabled by default in browsers and when NODE_ENV=test. A test run can therefore appear to have no trace even when production runs do. The same documentation says tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy. Treat that as an availability constraint, not as a signal that the agent did no work.
When using another framework, verify its own defaults. Microsoft’s guidance for agent-framework tracing identifies disabled content recording, an unset semantic-convention configuration, and operations that have no instrumentation as causes of incomplete or missing spans. These checks should happen before changing prompts or models.
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Inputs and outputs can contain personal data, secrets, proprietary documents, or regulated content. A trace payload should therefore be treated as production data, not as harmless debug text. Before enabling content recording or sharing a trace, review your organization’s retention, access, residency, and incident-response rules.
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- Prefer opaque identifiers, classifications, hashes, and redacted summaries when full payloads are unnecessary.
- Keep credentials, authorization headers, session tokens, and secrets out of span attributes and error messages.
- Separate access to metadata from access to recorded content where your platform permits it.
- Document which environments capture payloads and which capture only timing and status.
- Account for policy modes such as Zero Data Retention that can make a vendor’s tracing unavailable.
Neither a framework’s default nor a dashboard label establishes compliance for your organization. Your data-protection requirements determine what may be recorded and for how long.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Framework tracing or an OpenTelemetry-oriented setup?
Both approaches can be valid. Built-in tracing is usually closest to the framework’s own concepts; an OpenTelemetry-oriented setup can provide a common transport and analysis model across services. The practical choice depends on coverage and controls rather than a universal performance ranking.
| Decision axis | Built-in framework tracing | OpenTelemetry-oriented setup |
|---|---|---|
| Framework coverage | Often represents the framework’s model, tool, handoff, guardrail, and custom operations when supported. | Depends on the framework’s instrumentation package and what it emits. |
| Debugging fields | May expose framework-specific inputs, outputs, timing, and status in its native viewer. | Can carry standard span data, but emitted detail still depends on configuration and instrumentation. |
| Export and viewing | Uses the framework’s documented dashboard or trace-export API. | Uses an OpenTelemetry collector and a compatible backend or viewer. |
| Configuration effort | Often lower for a single framework, but runtime defaults and flush behavior still require verification. | Requires collector, convention, and backend decisions; cross-service consistency can be an advantage. |
| Missing-span diagnosis | Follow the framework’s coverage and troubleshooting documentation. | Check SDK instrumentation, semantic conventions, propagation, collector intake, and backend visibility. |
| Data controls | Use the framework’s content-recording and policy controls. | Apply capture, filtering, sampling, retention, and access controls across the telemetry pipeline. |
Microsoft documents OpenTelemetry integration for agent frameworks and describes traces, logs, and metrics emitted by supported configurations. That establishes interoperability, not that every framework emits identical spans or that one approach is superior.
Common failure scenarios and recovery moves
The run has no trace
Check whether the process is a browser, a test process with NODE_ENV=test, or an organization subject to a tracing restriction. Then verify exporter configuration, credentials, network access, and whether the run closed cleanly enough to flush buffered data.
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The trace appears only after a delay
Look for a buffered exporter or asynchronous worker. Confirm the documented flush or shutdown hook runs on normal completion and on error paths. During local debugging, keep the process alive long enough for export rather than terminating immediately after the response is returned.
The model span exists but the tool does not
Inspect the model’s recorded tool decision and the parent agent’s routing logic. If dispatch occurred, add or repair instrumentation around the tool adapter and propagate the trace context into downstream requests.
The trace stops at a guardrail
Read the guardrail status and recorded reason if policy permits. A blocked action is different from an unhandled exception; make the user-facing behavior explicit and keep the guardrail span so operators can distinguish intentional prevention from a crash.
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Compare the output contract at each boundary. Validate tool responses before handing them to the model, record transformations as custom spans, and identify the first output that lacks required data. If the trace does not include that transformation, the observability gap is itself the next defect to fix.
A repeatable checklist for the next failing run
- Locate the trace by correlation ID, session identifier, or request metadata.
- Verify that export completed and that the record is visible in the expected dashboard or API response.
- Inspect run status, start and end times, and total duration.
- Find the first failed, missing, cancelled, or unexpectedly slow span.
- Compare parent and child inputs and outputs when policy allows payload inspection.
- Check model generation, tool execution, handoff, guardrail, and custom-operation coverage.
- Review content-recording and semantic-convention settings if spans are incomplete.
- Confirm runtime defaults, test flags, and organizational tracing restrictions.
- Reproduce with a controlled input and the same workflow version after the evidence identifies a likely branch.
- Record the final cause and add instrumentation or validation that prevents the same blind spot.
Further reading
For distributed-tracing fundamentals such as instrumentation, collection, analysis, sampling, and context propagation, O’Reilly’s Distributed Tracing in Practice by Austin Parker, Daniel Spoonhower, Jonathan Mace, Ben Sigelman, and Rebecca Isaacs was published in April 2020 and runs 327 pages. Use current framework documentation for agent-specific span names, runtime defaults, export behavior, and data controls.
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