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What the triage layer needs to do
Think of triage as a decision layer between agent execution and recovery. Its input is a run’s trace and structured error context; its output is a recommended action and, where policy permits, an action the system can execute. It should preserve enough evidence for operators to understand the decision and should fail safely when evidence is missing or unfamiliar.
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Keep four concerns distinct:
- Observation: record what happened during the run.
- Classification: identify the failure stage and class without discarding the original error details.
- Policy: decide which recoveries are allowed under the run’s state and risk level.
- Evaluation: check whether the classification and recovery improved outcomes.
These are application architecture choices, not requirements for a particular agent framework. OpenAI’s tracing and recovery documentation provides examples for OpenAI-oriented workflows; the same design can be adapted to other systems.
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Instrument a run as an end-to-end trace
Assign each workflow run a stable trace or run ID and represent its meaningful operations as nested spans. OpenAI’s Evaluate agent workflows guide describes a trace as the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run. Include application events when they clarify what the workflow did or why it stopped.
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Record useful context on each event
At minimum, capture the workflow and stage, start and end times, status, and a structured error context when one exists. Trace model generations, tool calls, guardrail checks, handoffs, and relevant application actions. Correlate related events with the same run ID, and retain the provider’s error code and message rather than replacing them with a generic “agent failed” label.
A useful event model can add application-owned fields such as failure_layer, failure_class, retryable, tool_name, step_name, attempt, and recovery_action. Treat these as an example schema, not universal field names. Preserve unknown provider fields and tolerate missing ones: error codes and response shapes can change, and handlers should not crash or misclassify an event merely because a new code appears.
Protect sensitive data at the export boundary
Trace detail can include prompts, tool arguments, outputs, or personal data. Record only what is needed to diagnose and evaluate behavior. OpenAI’s Agents SDK documentation describes controls for omitting request inputs and outputs, as well as custom processor and exporter options. If policy requires redaction before telemetry leaves your application, perform it in an application-controlled exporter and fail closed if redaction cannot be completed; do not silently export unredacted data.
Classify both the failure stage and the error
A useful triage event answers two different questions: where did the run fail? and what kind of failure occurred? OpenAI’s API reference distinguishes request, turn, session, and environment failures. Extend that distinction to your own workflow, but keep the provider’s original code and message intact. The taxonomy below is a starting point, not a universal standard.
| Observed failure | Likely triage route | Why |
|---|---|---|
| Invalid request, schema, or configuration | Stop and identify the input or setting to correct. | Repeating an unchanged invalid request is unlikely to help. |
| Authentication, permission, or billing problem | Route to credential, access, or account remediation. | This is not a transient model failure. |
| Conflict or resource-state problem | Retrieve the current state before deciding whether to continue or retry. | The state may have changed since the operation began. |
| Rate limit, overload, timeout, or temporary service failure | Consider a bounded retry; honor Retry-After when supplied. |
The condition may be temporary, but repetition still needs a limit. |
| Unknown or incomplete error | Preserve the raw details and use a safe fallback or human review. | String matching on an incomplete or unfamiliar error is brittle. |
The table is a routing policy outline, not a claim that every provider uses the same codes or that a given code always has the same recovery. Use the source system’s structured error fields where available. Do not make a high-impact decision from a message substring when the structured code, stage, or run state is absent.
Make retries state-aware and bounded
A failed turn does not prove that no work occurred. A tool may have completed, a file may have been written, or the session may have advanced before an error surfaced. Before asking the agent to repeat work, inspect the turn or session, saved items, current run status, and relevant tool or file side effects. OpenAI’s Errors and recovery guidance specifically advises inspecting tool results even when a turn completes and stopping automatic retries if the error changes or the retry limit is reached.
Use an explicit recovery sequence
- Read the trace and original error. Identify the failing stage, error class, affected tool, and last confirmed successful action.
- Retrieve current state. Check whether the turn or session is still active, completed, or otherwise changed, and inspect saved results and side effects.
- Choose the least risky action. Correct invalid input or configuration; route access problems for remediation; refresh state for conflicts; or retry only a condition that policy treats as transient.
- Apply a hard attempt cap or deadline. Use an explicit delay policy, including
Retry-Afterwhen the service supplies it. Do not leave retries unbounded. - Stop when the error changes or the limit is reached. Reclassify a changed error rather than repeating the old recovery decision. Escalate or return a clear failure outcome when automation cannot proceed safely.
For operations with side effects, build idempotency and reconciliation into the application where possible. For example, the application can use its own operation identifier to check whether a requested action already completed before issuing it again. This is an application-level safeguard, not a specific idempotency mechanism prescribed by the cited recovery guidance.
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Guardrails should sit where risk is introduced, not only around the agent as a whole. OpenAI’s guardrails documentation describes input checks, output checks, and human approval, while noting that agent-level input and output guardrails run at specific chain boundaries. They may not cover every individual tool call.
- Before expensive or side-effecting work: validate inputs and reject disallowed requests.
- At each consequential tool call: validate arguments and, where appropriate, results. Apply a human approval step before sensitive actions.
- Before returning an answer: validate or redact output as required by the application’s policy.
Allow the triage layer to collect evidence and propose recovery, but require human approval for remediation whose impact exceeds the system’s authorization. A recovery classifier must not bypass the same checks that govern the normal workflow.
Evaluate the triage policy against real traces
Debugging one failure helps explain one run; evaluation checks whether a policy or workflow change works across examples. Start with representative traces and use structured graders to ask questions such as whether the correct tool was selected, a necessary handoff occurred, or the run followed policy. OpenAI’s workflow-evaluation guidance recommends moving from trace inspection toward datasets and repeatable evaluation runs once the team has a consistent definition of success.
Track measures your telemetry can actually support, such as failure rate by stage and class, retry frequency and success, cases left unresolved or escalated, time to triage, and side-effect incidents. These are suggested operational measures, not published benchmarks or universal targets. Compare changes on the same evaluation set where appropriate, and inspect examples behind a score before treating it as evidence that a recovery policy is safe.
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OpenTelemetry describes agent observability as fragmented and its GenAI semantic-convention work as evolving. Do not assume that every framework emits the same events or that a convention is settled across tools. Validate what your instrumentation actually records, especially for handoffs, tool outcomes, and error context.
Whether you begin with an SDK’s native tracing or an OpenTelemetry-centered setup, assess the same practical needs: which workflow events and failure details are captured; how sensitive data is controlled, redacted, and exported; whether the instrumentation fits the frameworks and backends already in use; and whether traces can support grading and repeatable evaluation. The cited documentation supports these comparison criteria, but does not establish a complete ranking of observability products.
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