AI Agent Traces: Debug Failures Without Keeping Raw Ticket Data
Keep workflow, tool, outcome, and timing signals for debugging without retaining raw ticket text or tool payloads in production traces.
For a production support-ticket agent, keep enough trace structure to identify the workflow version, model and tool steps, timing, error category, handoff, and outcome. Do not put full ticket text, model output, or tool request and response bodies in the default trace. If a difficult failure needs the original content, keep it in a separate, access-controlled record with its own owner and retention rule, then link it to the trace with an opaque reference.
That is a practical starting point, not a universal logging policy. OpenTelemetry's current GenAI conventions describe prompts and outputs as sensitive and often large, recommend not capturing them by default, and describe external storage with separate access controls for cases that need the content. Its attribute guidance also recommends making sensitive, verbose, or costly fields opt-in. (OpenTelemetry GenAI span conventions, OpenTelemetry attribute requirement levels)
What a support-agent trace needs to answer
When a ticket was not escalated, an engineer needs to see which release ran, which branch the agent took, whether the classification or tool call failed, and what final state the application recorded. The trace is operational evidence about a run. It is not the support ticket, a durable transcript, or the system of record for the customer interaction.
Use a small allowlist as the default trace contract. The fields below are recommendations for a SaaS support workflow; adapt them to your own incident questions and the data your systems classify as sensitive.
| Trace field | Keep by default | Keep out of the trace by default |
|---|---|---|
| Correlation | Random trace and run IDs; an opaque case reference that the support system can resolve under its own access checks | Email addresses, names, raw ticket IDs that expose customer identity, or caller-supplied IDs that may contain customer data |
| Release identity | Workflow and prompt-template version, application build, model/provider identifier, tool-schema version, and redaction-policy version | The full system prompt, ticket body, retrieved conversation, or rendered prompt |
| Model step | Operation name, duration, attempt number, token counts where available, and a bounded status code | Full model input, completion, or chain-of-thought-style content |
| Tool step | Tool name, operation class, duration, approved/denied state, and a stable result or error category | Raw arguments and result bodies, credentials, headers, or free-form exception text that can echo request data |
| Workflow outcome | Resolved, failed, incomplete, or awaiting-human status; a bounded reason code; and the handoff queue or step | A copied customer-facing response or internal case notes |
| Diagnostic content, when justified | An opaque pointer to a separately protected record, with a documented owner and expiry | A reusable URL, shared bucket path, or content copy with broader access or longer retention than the support record |
An opaque reference is only useful if the source application resolves it with the viewer's current permissions. A hash of an email or ticket number can still be linkable; do not treat hashing as proof that an identifier is anonymous. Keep any mapping between the trace reference and the case in the system that already enforces case access.
OpenTelemetry's guidance explicitly calls out prompt, completion, retrieval query, and tool-call data as potentially sensitive. OWASP's logging guidance likewise recommends excluding, masking, sanitizing, hashing, or encrypting sensitive information and controlling access to collected logs. These sources support a minimization approach; they do not decide your organization's data classification or retention period. (OpenTelemetry GenAI span conventions, OWASP Logging Cheat Sheet)
Built-in trace controls versus custom redaction
Check the runtime and observability backend you already use before adding an exporter or a second logging pipeline. Native controls are sufficient when they omit the sensitive fields you identified, cover every instrumentation and export path, and leave the team enough structured evidence to diagnose failures.
| Option | What current documentation says | When it can be enough |
|---|---|---|
| OpenAI Agents SDK trace settings | trace_include_sensitive_data is enabled by default. Setting it to False omits Responses API request input and response output from those model spans, while retaining the Responses response_id for official OpenAI endpoints. Function spans can also contain tool inputs and outputs. (Agents SDK tracing) | The sensitive fields are limited to data covered by the SDK setting, and the remaining response ID and run metadata are acceptable for your tracing destination. Verify the installed SDK version and all span types used by the workflow. |
| Langfuse masking | The current Python docs recommend mask_otel_spans for patching OpenTelemetry span attributes before the Langfuse client exports them, including spans from third-party instrumentation exported through that client. (Langfuse masking) | All relevant spans leave through that client and the masking rules cover their actual fields. Confirm that every exporter and attachment path uses the same policy. |
| Application-owned redaction and export | OpenAI's SDK docs warn that adding a redaction processor does not stop its default exporter if the redaction callback fails. For a redaction-dependent export, its documented pattern is to replace the processors and keep redaction and delivery inside one application-owned exporter. | Use this only when tests show a native control misses a required source, sink, or fail-closed behavior. Configure it before runs start; replacing processors does not erase data already buffered by an earlier processor. |
Avoid adding custom code just to copy fields your current platform already exposes. Add it for a demonstrated gap, such as a second exporter that bypasses the native mask, an instrumentation that records sensitive tool results outside the setting you chose, or a requirement to discard an export batch if redaction fails. Test the complete path from agent runtime through collectors and destinations, not just the first SDK hook.
Set retention for the trace and for every copy
Redaction answers what enters a trace. Retention answers how long each copy remains available. Keep those decisions separate from support-ticket retention: the operational trace, a replay artifact, an evaluation dataset, an export bucket, and a backup may all have different owners and deletion behavior.
For example, Langfuse documents project-level retention for traces and observations, while dataset items remain outside that retention policy. It also documents that trace deletion is asynchronous and recommends querying again to verify deletion. If a team saves a production trace into an evaluation dataset, deleting or expiring the source trace does not by itself remove that saved dataset item. (Langfuse data retention, Langfuse data deletion)
Treat those details as an example of why to inventory copies, not as a recommendation to use Langfuse. Confirm the current behavior for the exact SDK, backend, export bucket, and backup path in your stack. NIST's Generative AI Profile is voluntary risk-management guidance; it is not a legal determination or a universal retention schedule. (NIST AI 600-1: Generative AI Profile)
Release test: prove the trace is useful and bounded
Use synthetic tickets and deliberately fake canary values, never live customer records, to test the telemetry path. Exercise each point where text can enter or be transformed: request parsing, retrieval, model input/output, tool arguments/results, approval messages, errors, retries, asynchronous flush, and shutdown. Inspect every destination, not only the application console.
| Test case | Pass condition |
|---|---|
| Normal ticket contains a fake email, account number, and unique canary phrase | Trace contains run/version/tool/outcome fields but none of the canary values or raw message text |
| Retrieval result and tool result include separate canaries | Neither canary appears in spans, logs, collector output, exports, or saved evaluation examples unless a separately approved test path explicitly stores it |
| Tool fails with an exception that echoes its arguments | Trace records a bounded error category and linkage; the raw exception and argument values are absent |
| Human approval pauses and resumes the run | Trace connects the pause, decision result, resume, and final status without copying ticket text or reviewer credentials |
| Redaction code throws or a second exporter is enabled | No unredacted batch reaches any destination; the failure path does not log the payload or traceback |
| A saved evaluation example or exported copy is deleted or expires | The release record identifies each copy, expected deletion behavior, and how the operator verifies it |
Keep a release record with the tested SDK and backend versions, enabled trace settings, exporter list, fields allowed by the schema, test results, data owner, and the approved retention rule for each copy. This is an engineering test plan, not a security benchmark or a claim that a system cannot leak data.
For related decisions, the AI agent evaluation guide covers how to score sampled runs; the agent audit versus observability comparison explains the difference between a platform your team operates and a scored review. The separate support-ticket authorization guide covers what ticket content may authorize; this article focuses on what run data should be retained for debugging.
Where Zenovae helps
If an agent already runs in production, the Agent Reliability Audit is the relevant service hub for reviewing traces, failure cases, and release evidence. If telemetry has to cross the agent, support system, and existing observability stack, see AI integrations. For a workflow that still needs to be built, the production agent build page describes that scope.
Claim boundaries
The field list and tests above are engineering recommendations for a SaaS support-ticket workflow. They are not a security guarantee, certification, legal or compliance conclusion, Zenovae client result, or universal retention policy. SDK defaults, masking behavior, export order, and deletion semantics can change; verify the current documentation and test the deployed versions and all data paths you operate.
Sources
Need Help with Your AI Project?
At Zenovae, we build production-ready AI systems that scale. From OpenClaw setup to custom integrations, Mission Control workflows, and full-stack delivery, we can help you ship faster and avoid costly mistakes.
Let's Talk