Why AI Agents Hallucinate: How to Investigate and Reduce Unsupported Answers

Updated October 29, 2026. The guide now distinguishes task instructions from factual evidence, uses the current evaluator workflow and labels its example as illustrative. Unsupported hallucination-rate ranges and automatic-prevention promises have been removed.

An agent can give a plausible answer that its sources do not support. A retrieval-based support agent might invent a return window; a research agent might cite a paper absent from its evidence. These are examples of failures a team needs to detect, investigate and test against.

There is no universal hallucination rate or configuration switch that establishes factual correctness for every agent. The useful question is whether a specific answer is supported by the evidence and requirements for that task.

I’m Shashank Agarwal, founder and CEO of Noveum AI and creator of API.market. This guide describes my company’s workflow and was refreshed with AI assistance.

Separate the failure types

Several different problems can produce an answer a reviewer rejects:

• Missing or irrelevant retrieval: the material needed for the task did not reach the model.
• Unsupported generation: the answer adds a claim absent from the supplied evidence.
• Contradiction: the answer conflicts with the relevant source.
• Stale evidence: the source or model knowledge no longer reflects the question’s time period.
• Ambiguous task: the agent chooses an interpretation without enough information.
• Context loss: a relevant part of the conversation or document is omitted or truncated.

Do not infer a single cause from the final answer alone. Inspect the operations and context that actually produced it.

A system prompt is an instruction source

An instruction such as “use the supplied policy and say when information is missing” defines expected behavior. It does not prove the policy is current or that a claimed fact is true.

For instruction adherence, use the task and role constraints. For answer support, use the relevant captured source context. For checks that require an expected answer or reference, prepare one. A check described as reference-free can still require task, response, role or context fields.

It is also possible for a response to accurately repeat a wrong or outdated source. Distinguish consistency with supplied material from independent factual correctness.

Choose checks that fit the available evidence

Noveum’s public scorer library lists 112 published evaluators across 15 categories as of October 29, 2026. Teams select compatible checks for their data. The library count is not the number of checks automatically applied to every response.

For a retrieval-based answer, useful evaluation questions include:

Consult each scorer’s actual name, input requirements, result type and pass criteria in the catalog. Do not assume that similarly named checks have identical definitions or that all results share one numeric scale.

An illustrative policy example

Suppose a source says: “Returns are accepted within 14 days.” The user asks about the return window, and the agent answers: “You have 30 days and a money-back guarantee.”

A reviewer can identify two concrete issues: the number conflicts with the source, and the guarantee is unsupported. A useful saved finding would retain:

• the user’s question and relevant source excerpt;
• the generated answer;
• the check’s result and supporting explanation;
• the application version and evaluation configuration;
• the trace or dataset item needed to investigate the result.

No numeric scores in this example are claimed as observed benchmark or customer results. Whether an actual evaluator flags the response must be checked in a configured run.

Follow the failure back through the workflow

Use noveum-trace to capture the relevant instrumented operations, then prepare evaluation items through the platform’s dataset and ETL workflow. Confirm that the source context and answer were mapped correctly before interpreting a score.

NovaPilot can organize evaluated failures into findings and recommendations backed by the available evidence. Consider competing explanations: was the source wrong, was the wrong source selected, was important material omitted, or did the answer depart from adequate context?

A recommendation should guide a testable investigation. It does not prove the cause or guarantee that changing a prompt, model or retrieval setting will resolve the failure.

Test a candidate correction

A candidate change for the policy example might improve product filtering during retrieval or make the answer explicitly report missing policy information. Test it on the failing example and on other cases where the policy is available, absent or ambiguous.

Where appropriate, use configured NovaSynth personas and scenarios to exercise the candidate. Keep human-reviewed references for factual checks that need them. Inspect passing cases as well as failures so that false positives and missed problems are visible.

The eval-driven development guide describes that iteration loop, and the monitoring guide explains the production evidence workflow.

Questions that need task-specific answers

How common are hallucinations?

Use a rate measured on a stated task, dataset, model configuration and definition of failure. A percentage without that context is not a reliable estimate for your agent.

Can every hallucination be prevented before delivery?

No universal guarantee follows from tracing or scoring. An inline gate requires a separately verified execution path, acceptable latency and a defined failure policy. Evaluation of captured data is useful, but should not be described as an automatic gate on every response.

Should a team switch to a larger model?

Compare candidate models on the application’s representative cases, cost and latency requirements. Model size alone does not establish which one best meets a particular task.

What should happen when evidence is missing?

Define the intended behavior explicitly: ask for clarification, retrieve an approved source, qualify the answer or report the limitation. Evaluate that behavior rather than rewarding a confident completion unsupported by the task’s evidence.

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