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The “22 days” in the title is not enough, by itself, to establish what system was built or what it achieved. But it points to three questions that matter in any AI project: Where did an answer’s evidence come from? What did the evaluation actually test? What was the system allowed to do?
Those questions connect grounding, evaluation, and control. If evidence cannot be traced, a plausible answer may be unsupported. If tests cover only a narrow setup, a passing demo says little about other conditions. And if an agent has broad access or poorly defined permissions, even a capable system may take actions its designers did not intend.
How do you ground an AI answer?
Grounding means more than retrieving documents that share words with a question. For a consequential answer, a reviewer should be able to trace each important claim to evidence and determine whether that evidence actually supports it.
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Separate retrieval from support
Retrieval asks whether the system found relevant material. Faithfulness asks whether the answer’s claims are supported by that material. A search result can be relevant without proving the answer: it may mention the same topic while omitting a qualification, contradicting the claim, or supporting only part of it.
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For each consequential claim, check three things:
- Faithfulness: Does the cited source support the claim as written?
- Completeness: Does the answer preserve the source’s important message and qualifications rather than selecting a fragment that changes its meaning?
- Sufficiency: Is the evidence strong enough for the claim, or does the answer overstate what the source establishes?
Make the evidence trail inspectable
NIST’s agentic-evaluation probes project describes checking claims against a human-curated reference corpus and creating machine-readable audit trails that connect agent decisions to source evidence. NIST says the project page was created May 1, 2026, and updated May 5, 2026; it describes ongoing work, not a settled standard. Its aim is to move past accepting “the AI said so” and toward seeing what it found, where it found it, and how that evidence supports its conclusion.
In practice, record the claim, the source passage or data supporting it, and the system’s decision to use that evidence. When a claim is unsupported, incomplete, or stronger than the evidence, the trail should make that visible. This helps distinguish a retrieval failure from an answer that retrieved suitable material but misrepresented it.
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How do you evaluate an AI system?
An evaluation is evidence about performance under specified conditions—not a general guarantee. NIST’s Generative AI Profile, released July 26, 2024, cautions against extrapolating capabilities from narrow, non-systematic, anecdotal assessments and recommends documenting where results may not generalize beyond the system’s development conditions. NIST’s AI Risk Management Framework 1.0 is voluntary guidance and is being revised; it is not a regulation.
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Write down what the system is supposed to do, what counts as success, and what should count as failure before interpreting a score or a demo. A useful evaluation checks the intended task, not just whether the system produces a convincing-looking response.
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Test representative and adversarial cases
Use cases that reflect the system’s intended setting, alongside difficult inputs designed to expose weak assumptions, missing evidence, or unsafe behavior. A small set of easy demonstrations may show that a workflow can work; it cannot establish how reliably it handles the range of conditions it will encounter.
Inspect the harness, not just the result
The evaluation setup can shape what a system appears to know. NIST CAISI has documented solution contamination and grader gaming—for example, agents finding answer walkthroughs or exploiting loopholes in scoring. Its recommendations include reviewing transcripts, closing task-design loopholes, and standardizing which tools and actions agents can use. A score without that context may reward access to an answer or a quirk in the grader rather than the capability the test was meant to measure.
Record the model, tools, data, task design, scoring method, and permitted actions alongside results. Review failures and rerun the evaluation after meaningful changes to the model, prompt, data, tools, or harness. This makes a result interpretable as a finding about a particular setup, rather than an unqualified claim about an AI system.
Read benchmarks within their scope
OpenAI’s published account of a joint Anthropic–OpenAI alignment-evaluation exercise says difficult safety evaluations are not directly representative of real-world misbehavior. It also reports that relative model performance can vary by evaluation subset. Those findings make benchmark results useful for identifying behavior under tested conditions, but not a universal forecast or stable ranking across tasks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you keep an AI agent under control?
Control is a system-design problem: define the data and tools available to the agent, decide which actions it may take, and make failures visible enough to review and stop. A prompt can express intent, but the cited guidance does not establish that prompts alone guarantee control.
Bound access and actions
- Specify which data sources and tools the agent can access for the task.
- Define which actions it may complete on its own and which require human review.
- Keep evaluation conditions consistent about available tools and permitted actions, so results are not distorted by hidden differences in access.
Review source quality and guardrails over time
NIST’s Generative AI Profile recommends reviewing and verifying sources and citations in outputs, checking that retrieval-augmented generation (RAG) data is grounded, and regularly reviewing safety guardrails—especially under novel operating conditions. Treat these as continuing checks, not one-time setup tasks. New data, changed workflows, or unfamiliar operating conditions can make earlier assumptions unreliable.
Preserve a useful record of failures
Keep enough information to examine what the agent was asked to do, what evidence it used, which tools or actions were available, and what happened. Review transcripts and failures to see whether the problem came from weak evidence, a test loophole, overly broad permissions, or another cause. The point is not merely to stop a bad outcome, but to identify which boundary or assumption needs attention.
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Use the three questions as separate checks rather than treating a strong demo as proof that the whole system is reliable:
- Grounding: Can you trace important claims to evidence, and does that evidence support them fully?
- Evaluation: Do the test cases, grader, and available tools reflect what you want to measure, and have you documented the limits of the result?
- Control: Are data access and actions bounded, failures reviewable, and guardrails revisited when conditions change?
The exact tests and boundaries depend on the task, data, and deployment conditions. The cited guidance supports traceability, scoped evaluation, and ongoing review; it does not establish one universally reliable architecture or a guarantee that these practices eliminate failures.
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