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To monitor an AI agent for hallucinations, capture the full sequence of model, tool, and retrieval activity; record which source data was supplied; inspect representative failures; and rerun a repeatable evaluation set whenever the system changes. A trace can show how an answer was produced, but source attribution depends on your application retaining and connecting the retrieved records to that run.
What to capture in an agent trace
A final answer alone is not enough to diagnose an unsupported claim. Record the end-to-end sequence that produced it: model calls, tool calls, handoffs, guardrails, and relevant application events. OpenAI describes traces as a record of that activity, and its tracing documentation describes inspecting step inputs, outputs, duration, and status in a dashboard (OpenAI: Evaluate agent workflows; OpenAI Agents SDK: Tracing; OpenAI API: Tracing).
Use stable identifiers for runs and individual steps so an investigator can connect a user-facing response to the activity that preceded it. Include enough detail to determine whether the agent selected an appropriate tool, received useful results, followed the intended route, or encountered an error.
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For a retrieval-augmented or data-connected agent, keep provenance in your application’s run record. Trace visibility into workflow steps does not by itself guarantee that every application stores source IDs or links a particular answer claim to a retrieved document.
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- Record retrieval results. Capture stable identifiers for the records or documents returned, along with the retrieval context needed to understand why they were selected.
- Record what the model received. Preserve the relevant content, excerpts, or references actually passed into the model, not only the search query or a list of candidate documents.
- Keep version context. Store relevant timestamps or data-version information so a later reviewer can identify which version of a source was available during the run.
- Connect answers to evidence. In your application’s data model, associate the answer—or individual claims when needed—with the source records that support it.
- Expose the relationship for review. Make it possible to inspect the answer, retrieved evidence, and surrounding trace together. This helps distinguish a retrieval failure from a generation that ignored or misrepresented relevant evidence.
This is an implementation approach, not a universal provenance schema prescribed by the cited vendors. Choose a level of detail that allows investigation while meeting your organization’s data-handling requirements.
How to detect and investigate hallucinations
Use traces to find where a response became unsupported, then turn the failure into a test. OpenAI recommends examining representative traces and using trace grading to identify workflow issues and failure modes. For each sample, evaluate specific criteria rather than asking only whether the final answer “looks right.”
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Questions to ask when reviewing a trace
- Did the agent call the appropriate tool, or should it have retrieved data instead of answering directly?
- Did the tool return the expected records, and did the agent use those results?
- Did a handoff or routing decision occur when needed?
- Did the agent follow the relevant instruction or guardrail?
- Is each factual claim supported by the retrieved context, or does it add information that the sources do not establish?
- Did a tool error, missing record, or stale source contribute to the answer?
Use the findings to refine the prompt, tools, routing, retrieval behavior, or guardrails. A polished answer is not evidence of correctness; compare claims with the retained source context.
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Build repeatable evaluations
Once you have reviewed examples, preserve both successes and failures in a reusable dataset. Include cases that exercise different tools, retrieval outcomes, routing decisions, and unsupported-answer risks. OpenAI describes datasets and evaluation runs as a way to make workflow checks repeatable and compare changes over time (Evaluate agent workflows).
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Run the same evaluation set when you change prompts, models, retrieval, tools, or routing. Compare results against the previous run and inspect regressions instead of relying on an overall score alone.
Combine code checks, model graders, and human review
Arize Phoenix documents both deterministic evaluators—such as exact-match, regular-expression, or custom-heuristic checks—and LLM-as-a-judge evaluations. Its evaluation workflows can be applied to traces, experiments, datasets, and other data sources (Phoenix: Evaluation).
- Use deterministic checks when a criterion has a clear expected result, such as whether a required field is present or a known answer matches.
- Use a judge model for criteria that require contextual assessment, such as whether a response is relevant or supported by provided evidence.
- Use human review for high-impact claims, ambiguous cases, and targeted examples with known ground truth.
Treat evaluator results as signals for investigation, not proof that an answer is factually correct. A judge model can also miss an error, so retain examples that let people verify the result against source data.
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Monitor production and respond to trends
Track operational signals over time, including failed or unsupported answers, retrieval misses, tool errors, and evaluator outcomes. Set thresholds that trigger investigation, then review samples of the affected traces to find the underlying cause. A threshold or score is a monitoring aid, not a guarantee that all hallucinations will be detected.
Phoenix documentation distinguishes evaluation workflows from Arize AX Online Evals, which it identifies for production performance monitoring with alerting and threshold-based triggers (Phoenix: Evaluation). Check the current configuration and terms for any product you consider.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Tools documented for this workflow
| Option | Documented capabilities | Questions to check |
|---|---|---|
| OpenAI Platform and Agents SDK | Agent traces, trace inspection and grading, datasets, and evaluation runs. (OpenAI: Evaluate agent workflows; Agents SDK: Tracing; OpenAI 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 tracing and deterministic or LLM-judge evaluators; evaluation can cover traces and datasets. (Phoenix: Evaluation; Arize: Phoenix) | Which instrumentation integrations fit your stack? Can your team host or operate the setup as required? What evaluation and data-handling controls do you need? |
| Arize AX Online Evals | Phoenix documentation identifies it for production monitoring with alerting and thresholds. (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 which option is best for a particular company. Compare instrumentation fit, trace completeness, source-data handling, evaluator flexibility, production alerting, and operating requirements.
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