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To monitor an AI agent for hallucinations, record the full sequence of model and tool activity, preserve the data retrieved for each run, inspect failures, and repeatedly evaluate the workflow against examples with known outcomes. A trace can show how an agent reached an answer, but your application must also retain source identifiers and retrieval context if you want to connect that answer to specific records.
What to record in an agent trace
A useful trace captures the workflow, not just its final response. OpenAI describes traces as records of model calls, tool calls, guardrails, and handoffs; the Agents SDK documentation also covers custom events. Its dashboard can expose step inputs, outputs, duration, and status. See OpenAI’s Agents SDK tracing documentation and Tracing | OpenAI API.
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Give each run and step a stable identifier so you can connect an answer to the preceding activity. Capture the relevant inputs and outputs, tool requests and results, routing or handoff decisions, guardrail events, and application events. Apply appropriate access controls and retention limits: trace data may contain user inputs, retrieved content, or other sensitive information.
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How to trace an answer to its source data
Tracing workflow steps does not by itself prove which source records support an answer. For retrieval-augmented or data-connected agents, your application should save retrieval provenance alongside the run.
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- Record the stable IDs of retrieved documents or database records.
- Save enough retrieval context to determine what content or reference was supplied to the model, subject to your privacy and retention rules.
- Include relevant version, timestamp, or other context that lets you identify which data the agent saw.
- Connect answers—or individual claims where your application supports that level of detail—to the records intended to support them.
This is an implementation pattern, not a universal provenance schema prescribed by OpenAI’s tracing documentation. Design the link in your own data model; do not assume that an observability trace automatically includes or validates source citations.
How to detect hallucinations in an AI agent
Begin with representative traces, especially runs that produced incorrect, unsupported, or unexpected answers. OpenAI recommends inspecting traces and using graders to find workflow problems and failure modes. Its agent evaluation guide describes trace grading as a way to identify issues and improve workflows.
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Assess concrete questions rather than relying on a single vague “quality” score:
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- Did it call the right tool and use the result correctly?
- Did routing or a handoff happen when needed?
- Did the response follow the instructions?
- Are its factual claims supported by the retrieved context?
Separate a retrieval failure from a reasoning or response failure. If the needed evidence was never retrieved, investigate retrieval, indexing, or query construction. If the evidence was present but the answer contradicted it or went beyond it, inspect the prompt, model behavior, tool flow, and safeguards.
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Turn trace reviews into repeatable evaluations
Convert reviewed successes and failures into a dataset of cases with expected outcomes or review criteria. Rerun evaluations when you change prompts, models, retrieval, tools, or routing. Comparing results across changes can reveal regressions that a one-off trace review would miss.
Evaluators can use deterministic checks—such as exact matches, regular expressions, or custom heuristics—or an LLM judge. Arize Phoenix documents evaluation over production traces, experiment results, and datasets, with both deterministic and LLM-as-a-judge approaches in its evaluation documentation. These scores are signals for investigation, not proof of factual correctness: a judge model can also miss errors. For important claims, pair automated checks with human review and examples grounded in known source data.
Monitor production and investigate changes
Track operational and answer-quality signals over time, including unsupported answers, retrieval misses, tool errors, and evaluator outcomes. Choose thresholds that prompt investigation, then review affected samples and traces to find the cause. A score or alert can prioritize review; it cannot establish that every answer is safe or correct.
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| Option | Capabilities described in the cited documentation | Questions to assess fit |
|---|---|---|
| OpenAI Platform and Agents SDK | Agent traces, trace inspection and grading, datasets, and evaluation runs. See Evaluate agent workflows, Agents SDK tracing, and API tracing. | Does your application use the relevant OpenAI SDK or API? Which step inputs and outputs can you inspect? How will your application attach retrieval source IDs? |
| Arize Phoenix | Observability and evaluation, including deterministic and LLM-judge evaluators and evaluation over traces and datasets. See Phoenix and Evaluation. | Which instrumentation integrations fit your stack? Can your team operate the setup as required? What evaluation and data-handling controls do you need? |
| Arize AX Online Evals | Phoenix documentation identifies this for production monitoring with alerting and thresholds. See Phoenix Evaluation. | Do you need production alerting? Verify current configuration, availability, and terms. |
This is not a complete market comparison. The cited sources do not establish relative pricing, benchmark accuracy, or the best choice for a particular company. Compare instrumentation fit, trace completeness, source-data handling, evaluator flexibility, production alerting, and operating requirements.
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