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Evaluate an AI research agent on separate dimensions, not on fluency or the presence of footnotes alone. Use a fixed set of realistic questions, define the facts each answer must contain, check claims against trusted evidence, and audit every citation for faithfulness, completeness, and sufficiency. Record the system, date, tools, sources, rubric, and reviewers so another person can reproduce the comparison.
A polished report can still omit a required fact, misstate a source, or attach a citation that is only topically related. The procedure below gives you a practical way to measure what the agent said, whether it is true, and whether its evidence carries the burden of the claim.
What a reliable evaluation must measure
Answer quality has several dimensions that can move independently. An agent may produce complete answers with weak sources, accurate facts with missing citations, or excellent citations attached to an answer that leaves out a crucial part of the question. Keep the dimensions visible instead of hiding them in one overall score.
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|---|---|---|
| Answer completeness | Did the response cover every required fact or subquestion? | Required information nuggets present, including caveats and requested comparisons |
| Factual correctness | Are the stated facts correct against agreed reference evidence? | Claim-by-claim adjudication against authoritative sources |
| Citation faithfulness | Does the cited passage actually support the attached claim? | Reviewer can point to the supporting passage, not merely a related topic |
| Citation completeness | Does the answer preserve the source’s qualifications and full meaning? | Date, geography, population, limitations, and contrary findings retained when material |
| Evidence sufficiency | Is the source or combination of sources strong enough for the claim’s wording? | Authority, independence, quantity, and directness of evidence |
| Source quality and freshness | Are the sources appropriate, current, and preferably primary? | Publisher authority, publication date, original data, and fit to the question |
| Robustness | Does performance hold across realistic conditions? | Results by question type, domain, ambiguity, source age, and conflicting evidence |
| Reproducibility | Can another evaluator repeat the test and understand the judgments? | Version, date, prompts, tools, corpus, rubric, raw outputs, and adjudication notes |
These dimensions follow the distinctions made in the National Institute of Standards and Technology (NIST) framework for machine-generated reports and its agentic-AI citation probes. NIST describes citation quality in terms of faithfulness, completeness, and sufficiency; those terms are more informative than a binary cited/not-cited label.
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1. Define the use case before you test
Write down what the agent is expected to do and who will rely on it. A score has meaning only in the conditions for which it was designed. A tool used to summarize current regulation needs different tests from one used for historical background or literature synthesis.
Specify the operating conditions
- Audience: Identify whether the reader is a specialist, a general user, an analyst, or a decision-maker.
- Domain: Record the subject area and any terminology or language requirements.
- Recency: State the acceptable publication window. A current-policy question may require this year’s material; a historical question may not.
- Source expectations: Decide whether primary documents, peer-reviewed papers, official statistics, standards, or reputable secondary sources are required.
- Risk: Describe the consequence of an error. High-impact uses need stricter review and higher minimum thresholds.
- Human involvement: Say whether a person will verify every answer, sample outputs, or act on the report without checking it.
- Tool access: Record whether browsing, a controlled corpus, search APIs, calculators, code execution, or no external tools are allowed.
NIST’s AI Risk Management Framework (AI RMF) emphasizes realistic, representative tests and documented measurement methods. The AI RMF 1.0 excerpt dates from 2023 and notes that a revision is in progress, so treat it as guidance rather than a current universal policy requirement.
2. Build a question set with explicit answer requirements
Use a fixed set of questions drawn from real work or reader needs. For each question, prepare an adjudication sheet before running the agents. List the information a good answer must contain, acceptable variants, likely ambiguities, and known traps.
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An information nugget is one independently checkable piece of an answer. For a question about a new privacy rule, nuggets might include the effective date, covered organizations, territorial scope, user rights, exemptions, and the official enforcement source. Break compound requirements into separate nuggets so an answer cannot receive full credit for mentioning a topic while omitting a decisive detail.
Include different question shapes
- Factoid: asks for one date, definition, number, or named entity.
- List: requires several items, such as all eligibility conditions or documented limitations.
- Comparison: asks the agent to apply the same criteria to two or more options.
- Synthesis: requires evidence from multiple documents and an explanation of agreement or disagreement.
- Why, how, or what: elicits a long-form explanation with causal or procedural claims.
- Uncertainty case: has incomplete, conflicting, or time-sensitive evidence and should produce a qualified answer rather than false certainty.
The ALCE benchmark illustrates why one question type is not enough: its experiments include factoid, list, and long-form questions. Keep a reference answer or adjudication notes with the sources that justify each nugget. This lets reviewers distinguish a genuine omission from an acceptable wording difference.
Seed difficult cases deliberately
Add questions containing ambiguous names, multiple valid interpretations, conflicting studies, rapidly changing facts, and evidence that is absent from the permitted corpus. Also include a question where the correct response is that the available evidence is insufficient. An agent that always produces a definite answer should lose credit for inventing certainty in that case.
3. Run comparable trials
Comparison is fair only when systems face equivalent conditions. Use the same questions and, as far as practical, the same prompt instructions, date, source corpus, browsing permissions, output-length limit, and retry policy.
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- Freeze the test plan. Store the final question set, nugget lists, prompt, scoring rules, and source policy before collecting outputs.
- Record the environment. Capture the product and model version when shown, test date and time, enabled tools, retrieval settings, language, and regional settings.
- Control retries. Give each system the same number of attempts. If you select a best-of-many answer, state that selection rule; otherwise retain the first output.
- Preserve raw material. Save the complete answer, citation list, retrieved passages or URLs, and any intermediate research trace that reviewers are allowed to inspect.
- Mark live-web tests. Web results change. Record the retrieval date and rerun at planned intervals if freshness matters.
- Separate training from evaluation. Do not let test questions or their reference answers enter prompts, examples, or a tuning process used by the evaluated system.
This protocol is a practical application of NIST’s recommendations for representative test sets, documented methods, external validity, and robustness. It is not a claim that NIST mandates this exact trial format.
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4. Score the answer before inspecting its citations
First judge what the response says. This prevents an impressive reference list from masking an incomplete or incorrect answer.
Completeness
Mark each required nugget as present, absent, or partially present. A nugget is present only when the answer gives enough detail to satisfy the requirement. A passing mention that omits a required date, scope, exception, or condition is partial, not complete.
Factual correctness
Break the output into factual claims and compare each with the agreed reference evidence. Distinguish a wrong claim from an unsupported inference and from a statement that is true only under a condition the answer failed to state. Record disagreements rather than forcing every claim into a true/false category when the evidence itself is unsettled.
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Framing and uncertainty
Check whether the answer separates established facts, interpretation, inference, and uncertainty. A technically true sentence can mislead if it presents a preliminary result as settled, hides a major exception, or implies a causal relationship that the source only describes as an association.
Scope and reader fit
Verify that the response answers the requested question rather than a nearby one. Penalize irrelevant detail that crowds out required information, unexplained specialist language for a general audience, and recommendations that exceed the evidence or the stated scope.
5. Audit citations one claim at a time
Do not treat a citation marker or plausible-looking bibliography entry as proof. Open the cited document and inspect the passage attached to each material claim.
Identity and accessibility
Confirm that the reference is the document named by the agent and that a reviewer can reach the relevant passage. Note broken links, paywalls that prevent verification, missing page numbers for long documents, and citations that point only to a home page or search result.
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Ask whether the cited passage actually supports the claim as written. A source that discusses the same subject but does not state the claimed number, cause, population, or conclusion is topically related, not faithful evidence.
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Completeness of the citation’s meaning
Check whether the answer retained material qualifications: publication date, geography, sample, definition, confidence interval, exception, limitation, or contrary finding. Omitting one of these can reverse the practical meaning even when the quoted words are technically present in the source.
Sufficiency
Judge whether the evidence carries the claim’s evidentiary burden. A single opinion may support what an organization believes but not an assertion about an established fact. A preliminary study may support a tentative finding but not a universal recommendation. For a broad claim, several independent and authoritative sources may be necessary.
Attribution and authority
Check that the answer distinguishes a source’s assertion from independently established fact and that the source has the authority and date needed for the claim. An official page is usually appropriate for an agency’s current rule, while an original dataset or peer-reviewed study may be better for a scientific result.
Keep a short rationale for every verdict. Another reviewer should be able to see why a citation was judged faithful, incomplete, or insufficient without repeating the entire investigation.
6. Use transparent metrics instead of one magic score
Define denominators and grading rules before comparing systems. The following measures are useful operational choices; they are not universal NIST metrics.
| Measure | Suggested calculation | Interpretation |
|---|---|---|
| Nugget completeness | Required nuggets present ÷ total required nuggets | How much of the requested answer was covered |
| Claim correctness | Claims judged correct ÷ factual claims checked | Accuracy of what the answer asserted |
| Citation faithfulness | Cited claims with direct support ÷ cited claims audited | Whether attached passages support their claims |
| Citation completeness | Cited claims preserving material source qualifications ÷ cited claims audited | Whether context and limitations survived summarization |
| Evidence coverage | Material claims with a usable citation ÷ material claims identified | How much of the answer is verifiable from its references |
| Source quality | Predefined quality ratings by source, reported by category | Authority and freshness of the evidence selected |
| Robustness | Performance spread across predefined test segments | Whether averages conceal weak conditions |
Report the number of questions, claims, and citations behind every percentage. A result of 90% over ten claims is not equivalent to 90% over a thousand. Decide in advance how to handle partial nuggets, disputed claims, duplicate citations, and answers that correctly say evidence is insufficient.
Use thresholds with context
If a deployment requires one decision score, set weights and minimum floors before viewing results. For example, a high-risk workflow might require every critical nugget, a high claim-correctness floor, and zero unsupported claims on a sampled audit, while allowing a lower average on noncritical background details. Document who selected the weights and connect them to the consequences of error. Human judgment is necessary because acceptable trade-offs differ by use case.
7. Test robustness and external validity
A system’s overall average can hide failures that matter in practice. Break out results by conditions that match expected use:
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- Question type: factoid, list, comparison, synthesis, and long-form explanation.
- Domain and language, including specialized terminology.
- Source age and whether information is stable or rapidly changing.
- Ambiguous wording, conflicting sources, and multi-source reasoning.
- Questions with missing evidence or an answer that should express uncertainty.
- Retrieval conditions, such as a controlled corpus versus live web search.
- Output length and the presence or absence of a human verifier.
NIST describes robustness and generalizability as maintaining performance across circumstances and recommends realistic testing. Report segment results and confidence or uncertainty where appropriate instead of presenting only one mean.
8. Compare two or more agents on the same axes
Use one comparison table with identical definitions for every system. Keep the axes separate so a strong citation workflow does not compensate for poor factual coverage, or vice versa.
| Axis | What to inspect | Decision question |
|---|---|---|
| Answer completeness | Required facts and subquestions covered; important omissions | Does the reader receive everything needed? |
| Factual correctness | Claims checked against agreed reference evidence | Are the statements true under the stated conditions? |
| Citation faithfulness | Passage directly supports attached claim | Can a reviewer verify the claim in the cited text? |
| Citation completeness | Qualifications and context preserved | Would the source’s meaning survive the summary? |
| Evidence sufficiency | Strength and quantity of sources relative to claim | Is the evidence strong enough for the wording? |
| Source quality and freshness | Authority, date, originality, and recency | Are these the right sources for this question? |
| Robustness | Performance across difficult segments | Does it remain dependable when conditions change? |
| Reproducibility and transparency | Inspectable methods, outputs, rubrics, and judgments | Can the result be repeated and challenged? |
If stakeholders insist on a single winner, publish the underlying axis scores, the weights, the minimum thresholds, and the consequences of failing each threshold. A weighted rank without those details is difficult to interpret and easy to game.
9. Publish an evaluation record others can inspect
A credible report explains how the result was produced. Include:
- System and model version, when known, plus the evaluation date.
- Question set or its construction method, including nugget definitions and edge cases.
- Prompt, language, output limits, retry policy, and enabled tools.
- Source corpus or retrieval conditions, with source dates and access restrictions.
- Scoring rubric, partial-credit rules, denominators, aggregation method, and thresholds.
- Raw outputs and citation lists, subject to privacy, copyright, and security constraints.
- Reviewer qualifications, number of reviewers, disagreement process, and adjudication notes.
- Known limitations, excluded questions, and changes made between test rounds.
Automated judges are measurement instruments too. Have human reviewers inspect a sample of automated verdicts, especially borderline cases, and report where the judge disagreed with people. NIST’s agentic-AI probe project describes rubric-based probes that compare outputs with trusted material and return verdicts with rationales; it does not establish a universal error rate for automated judges.
10. What published evidence can and cannot tell you
The ALCE 49% result
In the ELI5 experiments reported by Gao, Yen, Yu, and Chen in the 2023 ALCE paper, 49% of ChatGPT baseline generations were not fully supported by the cited passages. This is a result from that benchmark and its experimental conditions. It demonstrates why citation presence is not the same as citation support; it is not a current, universal failure rate for AI research agents and should not be used to rank today’s tools.
NIST’s report-evaluation framework
The paper On the Evaluation of Machine-Generated Reports was published July 14, 2024 in the Proceedings of ACM SIGIR 2024. Mayfield and coauthors describe required information nuggets for completeness and accuracy, along with claim-to-document citation evaluation for verifiability. The framework supplies a useful way to structure tests, not a universal pass score for every product.
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NIST’s citation probes
The NIST project page Building Evaluation Probes into Agentic AI, updated May 5, 2026, says its demonstration probes cover citation faithfulness, completeness, and sufficiency. The project is ongoing and represents an emerging approach rather than a settled cross-agent standard or leaderboard.
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11. Common failure modes and corrective actions
Counting references instead of checking support
Failure: A report receives credit because every paragraph has a footnote. Correction: Audit each material claim and require a supporting passage.
Using one broad score
Failure: High fluency or source quality hides missing facts and unsupported conclusions. Correction: Publish completeness, correctness, citation, source, and robustness results separately.
Ignoring omitted qualifications
Failure: A date, population, exception, or limitation disappears during summarization. Correction: Put those conditions in the nugget list and citation-completeness rubric.
Testing only easy questions
Failure: Short factual prompts create an unrealistic impression of reliability. Correction: Add synthesis, ambiguity, conflict, missing-evidence, and uncertainty cases.
Changing conditions between systems
Failure: One agent gets browsing, more retries, or a newer source corpus. Correction: Freeze equivalent prompts, tools, dates, retry limits, and retrieval conditions.
Trusting an automated grader without calibration
Failure: The evaluator’s own mistakes become the reported result. Correction: Compare a sample of automated judgments with human review and document disagreements.
Overgeneralizing old benchmark numbers
Failure: A figure from a 2023 benchmark is presented as a present-day product rate. Correction: State the dataset, task, date, and experimental scope whenever the number appears.
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Use this sequence when selecting an agent for a real team or publication process:
- Define the audience, domain, freshness requirement, source policy, risk, and human-review plan.
- Create a frozen question set with required nuggets, accepted answers, traps, and reference evidence.
- Run every candidate under equivalent prompts, tools, dates, corpus conditions, output limits, and retry rules.
- Score completeness, correctness, uncertainty handling, and reader fit without looking at citations first.
- Audit each material claim for citation identity, faithfulness, completeness, sufficiency, and attribution.
- Calculate the predefined measures with transparent denominators and record reviewer rationales.
- Break out results by question type, domain, source age, ambiguity, conflict, and missing evidence.
- Apply predeclared minimum thresholds and weights that reflect the consequences of error.
- Inspect automated grading against human judgments and adjudicate material disagreements.
- Publish the methods, limitations, raw or reviewable evidence, and a schedule for rerunning the test as systems and sources change.
Re-evaluate after a model update, retrieval change, prompt change, or major shift in the source environment. A score is a snapshot of a defined setup, not a permanent property of an agent.
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