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Build an automated triage layer by tracing each agent run end to end, classifying failures by both where they occurred and what kind they are, then routing them to a bounded recovery action. A safe system does not blindly retry a failed turn: it checks run state and completed side effects first, and sends sensitive or uncertain cases to a human.
What the triage layer should do
The triage layer sits between agent execution and recovery. It gathers evidence about a run, applies a routing policy, and either allows a safe next step or pauses the workflow. Treat it as a policy engine—not as another agent with permission to bypass the controls on the original workflow.
A practical decision path is:
- Record the run as a trace containing its meaningful model, tool, guardrail, and handoff steps.
- Normalize the failure while retaining the provider’s original code and message.
- Check whether the run is still active, completed, or partly completed, and inspect relevant side effects.
- Choose among correcting input or configuration, bounded retry, continuing from verified state, evaluation, or human review.
- Record the outcome so the routing policy can be assessed and improved.
This pattern is especially useful when a workflow has multiple steps: a generic “agent failed” event does not tell an operator whether to fix a malformed request, restore access, wait out a transient service problem, or investigate an action that may already have happened.
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Use one trace per end-to-end run
Represent a workflow as a trace with nested spans. Give each run a stable trace or run ID, and record the workflow name, span or stage type, start and end times, status, and structured error context. Capture the events that explain the outcome: model generations, tool calls, guardrail checks, handoffs, and application-specific steps.
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OpenAI’s workflow-evaluation guidance describes a trace as the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run. Its Agents SDK documentation uses span types such as agent, generation, function, guardrail, and handoff. Those are useful examples, not requirements for every framework; use names that preserve meaning in your own system.
Keep telemetry useful without turning it into a secret store
Include enough context to diagnose a failure, but avoid putting unnecessary credentials, raw personal data, or sensitive tool payloads into telemetry. OpenAI’s SDK documentation describes controls for omitting request inputs and outputs, as well as custom processor and exporter options. Decide which fields are safe to retain, who can access traces, and how long they are kept.
If policy requires redaction before telemetry leaves your application, perform that redaction in an application-controlled exporter and fail closed if redaction fails. Do not assume a downstream dashboard will remove data that has already been exported.
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Store two things independently: the failure layer—where execution failed—and the failure class—what kind of problem it appears to be. A normalized event should retain the provider’s original error code and message, even when you add application-owned fields for routing. This lets operators use a stable internal policy without losing the detail needed to investigate a particular provider error.
A starting event shape might look like this. It is an application design example, not a provider-mandated schema:
{
"run_id": "stable-run-id",
"workflow": "order_assistance",
"stage": "tool_call",
"failure_layer": "environment",
"failure_class": "timeout",
"provider_error_code": "original-code-or-null",
"provider_error_message": "original-message-or-null",
"tool": "inventory_lookup",
"retryable": true,
"attempt": 1,
"status": "failed",
"occurred_at": "timestamp",
"side_effect_check": "not_checked"
}Keep the provider fields intact rather than replacing them with your normalized labels. Error codes and fields can vary; OpenAI’s API reference advises handlers to tolerate unknown codes and missing fields. Avoid brittle policies that rely only on matching message text.
Distinguish at least these layers
- Request or configuration: the submitted request, schema, or setting is invalid or incompatible.
- Turn: the current model-and-tool interaction did not complete as expected.
- Session: the broader conversation or run state is unavailable, inconsistent, or otherwise involved in the failure.
- Environment: a dependency such as a tool, permission system, or external service prevented progress.
This is a useful implementation taxonomy, not a claim that every provider uses these exact categories. Keep the raw provider details alongside it so your handler can accommodate new codes and provider-specific distinctions.
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Route errors to a safe next action
Start with a small, explicit routing table. The action should reflect the failure evidence, not a generic assumption that every error is transient.
| Observed problem | Initial action | Do not do this automatically |
|---|---|---|
| Invalid request, schema, or configuration | Stop and report the field or setting that needs correction. | Resubmit unchanged input and expect it to succeed. |
| Authentication, permission, or billing problem | Route to credential, access, or account remediation. | Classify it as a temporary model error or keep retrying. |
| Conflict or resource-state problem | Retrieve the current state and decide whether the workflow can continue or needs a corrected action. | Repeat an operation against state that may have changed. |
| Rate limit, overload, timeout, or temporary service failure | Consider a bounded retry; honor Retry-After when supplied. |
Retry indefinitely or ignore a server-provided delay. |
| Unknown code or incomplete error fields | Preserve the raw details and use a safe fallback, often human review. | Crash the handler or infer a retry policy from an unrecognized string. |
Depending on the verified run state, a routing outcome can be a corrected request, a retry, continuation from a known completed step, an evaluation case, or a human handoff. If the state or likely impact is unclear, pause rather than guessing.
Make retries state-aware and bounded
A failed call or turn is not proof that nothing happened. Before repeating work, inspect the relevant run state and completed actions. OpenAI’s Agents API recovery guidance recommends checking tool results even when a turn completes and stopping automatic retries if the error changes or the retry limit is reached.
- Identify the failed step. Use the trace to locate the failing span and its tool, input, and preceding events.
- Retrieve current state. Check whether the turn or session remains active or completed, and inspect saved items and relevant tool results.
- Check for side effects. Establish whether an external action, such as a write or submission, already happened. If you cannot determine that safely, stop for review.
- Confirm retry eligibility. Retry only if the failure is plausibly transient, the input or configuration has not remained invalid, and repeating the operation is safe.
- Enforce a limit. Set an explicit attempt cap or deadline and a delay policy. Honor
Retry-Afterwhen supplied. - Reassess after each attempt. Stop if the error class changes, the limit is reached, or the new trace shows an uncertain or completed side effect. Record whether the retry resolved the issue.
For operations that change external state, design idempotency and reconciliation into the application where possible. The specific mechanism depends on the tool and system; the essential requirement is to avoid duplicating an action simply because an agent turn reported failure.
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Put risk checks at the tool boundary
Apply controls where the risk enters or leaves the system, not only at the start and end of an agent chain. OpenAI’s guardrails guidance describes input checks before work, output checks before delivery, and human approval for sensitive actions. It also notes that agent-level input and output guardrails run at particular chain boundaries; they do not necessarily inspect every tool call.
- Reject disallowed input before expensive or side-effecting work begins.
- Validate or redact output before it is delivered.
- Validate function arguments and results around tool calls.
- Require human approval before sensitive actions, and keep that approval requirement in force during automated recovery.
The triage layer may gather evidence and propose a recovery, but it should not be able to bypass the controls governing the normal workflow. Escalate high-impact remediation when an automated decision cannot establish that the next action is safe.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate the triage policy, not only the agent
Turn representative traces into test cases
Use traces to debug individual failures, then turn recurring quality criteria into repeatable evaluations. OpenAI’s workflow-evaluation guidance describes using trace grading to assess an end-to-end run and assembling traces into datasets for evaluation across changes.
Useful grader questions include whether the workflow selected the right tool, handed off when needed, followed policy, and improved end to end after a prompt or routing change. Include both successful and failed runs, and make sure the examples cover cases where retrying would be unsafe as well as cases where a bounded retry is reasonable.
Measure operational outcomes
Compute measures from your own traces rather than assuming an industry target. Useful measures include failure rate by stage and class, retry frequency and success, cases left unresolved or escalated, time to triage, and incidents involving unintended side effects. Define each measure consistently—for example, what counts as a successful retry—so changes in routing policy can be compared meaningfully.
Keep instrumentation portable and data handling deliberate
OpenTelemetry describes agent observability as fragmented and its GenAI semantic-convention work as evolving. Treat conventions as a moving target: validate what your framework emits, and do not assume that a span name or field has the same meaning across libraries.
Keep a clear instrumentation and export boundary so traces can feed the backend your team chooses. When comparing an SDK-native tracing path with an OpenTelemetry-centered or hosted approach, assess which workflow events and failure details are captured, how sensitive data is controlled and exported, compatibility with your existing frameworks and backends, and whether trace grading and repeatable evaluation fit your process. The documented material does not establish a universal vendor ranking; make the choice against your requirements.
Quick Recap
Roll it out in stages
- Instrument first. Capture end-to-end traces and structured failure context, with a deliberate policy for sensitive fields.
- Classify without acting. Run the proposed routing policy in observation mode and compare its classifications with operator decisions.
- Automate low-risk recovery. Enable bounded retries only for cases where state is checked and repetition is safe.
- Gate consequential actions. Require approval or a safe fallback when a recovery could create a sensitive side effect or the state is uncertain.
- Evaluate changes. Use representative traces and repeatable graders to assess both agent behavior and triage decisions as the workflow changes.
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