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The clearest lesson from 22 days of building AI systems is that a convincing answer is not enough. I need to be able to trace its claims to evidence, know what an evaluation actually tested, and constrain what the system can do. Those are connected engineering problems—not guarantees that a short build period can establish an AI system’s general reliability.
How do I know what an AI answer is grounded in?
Start by treating an answer’s important claims as things that need evidence, not just fluent wording. For each consequential claim, preserve a trace to the source material the system used. Then check whether that material actually supports the claim.
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This is different from checking whether retrieval found a document with similar words. Retrieval quality asks whether the system found relevant material; answer faithfulness asks whether the answer accurately represents that material. A useful grounding check asks three questions:
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- Completeness: Does the answer preserve the source’s full message, including qualifications that change its meaning?
- Sufficiency: Is the evidence strong enough for the claim, or does it only suggest a possibility?
NIST’s Building Evaluation Probes into Agentic AI, a project page created May 1, 2026 and updated May 5, 2026, describes evaluating claims against a human-curated reference corpus and creating machine-readable audit trails that connect decisions to source evidence. It captures the point well: “The goal is to move beyond ‘the AI said so’ to better understand ‘here is what the AI found, where it found it, and how the evidence supports the conclusions.’” The page describes ongoing project work, not a settled standard.
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In practice, a useful evidence trail should let a reviewer move from a claim to the source passage and judge the relationship between them. A citation that merely points to a relevant-looking page is not proof that the page supports every sentence attached to it. This matters especially when a system summarizes several sources, combines evidence, or turns a qualified statement into a definite one.
What does an AI evaluation actually tell me?
An evaluation tells you how a system performed under the tasks, data, tools, scoring rules, and conditions used in that evaluation. It does not automatically tell you how the system will behave on different users’ requests, new data, or unfamiliar operating conditions.
NIST’s Generative AI Profile, released July 26, 2024, warns: “Avoid extrapolating GAI system performance or capabilities from narrow, non-systematic, and anecdotal assessments.” It also recommends documenting the limits of generalizability beyond the conditions under which a system was developed. NIST’s AI Risk Management Framework is voluntary-use guidance, not regulation; NIST says it is being revised.
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Define the task and success condition
Before scoring outputs, write down what the system is supposed to do and what counts as success. Make the success condition observable: for example, whether it used an authorized source, produced a supported answer, or correctly declined when evidence was inadequate. A vague target makes it hard to tell whether a passing result reflects useful behavior or simply an easy test.
Test representative cases and difficult edges
Use examples that resemble the intended work, along with cases likely to expose failure: missing evidence, conflicting sources, ambiguous requests, and attempts to elicit an unsupported answer. Keep the test set and operating conditions in view when interpreting a score. A benchmark result is evidence about tested conditions, not a universal forecast.
Inspect the harness, not just the score
The evaluation setup can shape results. NIST’s Center for AI Standards and Innovation (CAISI) has documented solution contamination and grader gaming, including agents finding answer walkthroughs or exploiting scoring loopholes. It recommends reviewing transcripts, closing task-design loopholes, and standardizing which tools and actions agents may use.
That makes failure review part of evaluation, not optional cleanup. An unexpectedly successful run may show that the system solved the intended task—or that the task exposed an unintended shortcut. Inspecting how the result was produced helps distinguish the two.
Record the conditions and rerun after changes
Keep track of the model, data, tools, permissions, prompts, and scoring procedure used for a run. If any of these change, the earlier result may no longer describe the current system. Rerunning relevant cases after a change is the practical way to learn whether a fix helped, introduced a different failure, or left the original problem intact.
OpenAI’s published account of a joint Anthropic–OpenAI alignment evaluation exercise makes an important scope distinction: difficult safety evaluations are not directly representative of real-world misbehavior, and relative model performance can vary across evaluation subsets. Its authors wrote: “Therefore, we are continually updating our evaluations to make them ever more challenging, and move beyond any evaluation where models perform perfectly.” The exercise is evidence about its named models and test conditions, not a durable overall ranking.
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How do I keep an AI agent under control?
Control is a system-design question: what information can the agent access, which tools can it use, what actions can it take, and when does a person need to review or approve an action? A prompt may express the intended behavior, but it is not, by itself, evidence that these boundaries will hold in every situation.
Set boundaries around data and actions
Grant access only to the data and tools needed for the task. Decide which actions may happen automatically and which require review. Make those boundaries explicit in the operating setup, rather than relying only on the agent to infer them from a general instruction.
Preserve provenance and review safety behavior
Control also depends on knowing where the system’s information came from. NIST’s Generative AI Profile recommends reviewing and verifying sources and citations in outputs, checking that retrieval-augmented generation data is grounded, and regularly reviewing safety guardrails—especially in novel operating conditions. A guardrail that worked in a familiar workflow may need reassessment when the data, tools, or surrounding task changes.
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Make failures visible and containable
Log enough of the system’s decisions, evidence, and tool use for a reviewer to understand what happened. Define how to pause, stop, or escalate an operation when it reaches an uncertain or unauthorized case. Without visibility into the path from input to action, a correct-looking final answer can conceal an unsupported claim or an unsafe step.
What should I carry into the next build?
A 22-day build can surface engineering questions and expose weaknesses; the calendar alone cannot settle how a system will perform elsewhere. The practices I would carry forward are specific:
- Attach important claims to inspectable source evidence, and check support rather than keyword overlap.
- Define evaluation success before running tests, then include representative and adversarial cases.
- Review transcripts and scoring rules for shortcuts that let a system pass without doing the intended task.
- Record the model, data, tools, permissions, and harness conditions behind each result.
- Constrain tool and data access, require review for consequential actions, and revisit guardrails as operating conditions change.
The unresolved question is always system-specific: whether the evidence, tests, and boundaries are adequate for the particular task and consequences. These methods make that judgment more inspectable; they do not remove the need to make it.
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