How to Test an AI Agent Before It Builds for a Client

Before letting a coding agent turn a client request into a build specification, test whether it can discover the missing requirements. Can it inspect the existing project, ask consequential questions and distinguish a confirmed fact from an assumption? Producing plausible code does not answer those questions.

Research on clarification and interactive development provides useful test designs, but it does not establish that an agent understands any real client’s needs. This guide proposes a controlled discovery exercise for agency owners and product leads. It reports no original Digital Applied comparison or client study.

A request for a dashboard may hide several possible outcomes: finding late orders, reconciling invoices or deciding where to allocate staff. The agent should establish which decision the client needs to make before committing to a design. It should also inspect existing behavior that the new work must preserve.

The evaluation should reward evidence, not the length of the specification. Each consequential requirement needs a source in the supplied packet, repository or client response. A sensible-sounding feature without that support is still an assumption.

Our specification-quality guide explains how to write a useful brief. The preceding question is different: can the agent recover the facts and resolve the ambiguities needed to write that brief?

The September 4 hyper-tau-bench paper combines business records, simulated client interaction, inherited code and operational constraints. Its reported outcome is a budget-adjusted construction score, not a direct measure of interview accuracy. The expert reference has privileged information, and the client is simulated. These conditions limit what the results establish about real engagements.

The ClarifyGPT paper treats requirements clarification as a distinct intervention before code generation. Its programming-task setting should not be stretched into evidence of autonomous client-project competence. It does support asking whether a workflow identifies ambiguity instead of guessing.

UserBench examines task completion alongside discovering user preferences through simulated interaction. The requirements follow-up question study evaluates generated questions as an aid to elicitation. Together, these studies motivate separating useful questioning from successful implementation; they do not supply a universal agent success rate.

Do not infer that asking more questions causes better results from a correlation between question count and scores. A harder task, a better agent or a different interaction budget can affect both. Ask what was controlled and what the study actually measured.

Start with a small fictional client problem, a read-only repository snapshot and a short set of business records. Keep a separate evaluator key containing the intended requirements, acceptable alternatives, contradictions and facts that are genuinely unknown. Do not use private customer records for the initial exercise.

Include a fact the agent can find by inspecting the project, a fact it must ask the client to supply and a fact no participant is authorized to invent. This creates a meaningful distinction between useful inspection, useful questioning and appropriate uncertainty.

Have a reviewer examine the key before the run. If the key assumes one implementation even though several satisfy the client’s outcome, it may punish a valid solution. Record accepted alternatives and the authority needed to resolve conflicting priorities.

The ICAE-Bench paper uses a grounded user simulator and executable project material to investigate interactive coding. That structure is useful inspiration. Repository-derived hidden requirements still differ from a live relationship where stakeholders may not yet agree on their goals.

Use separate clean contexts and the same project packet to compare evidence inspection, interviewing and their combination. Freeze the model, harness and available time or interaction budget. The purpose is to identify which information source improves the specification, not to recommend withholding normal context in production.

For each attempted run, record requirements correctly recovered, unsupported requirements introduced, contradictions surfaced and critical unknowns acknowledged. Preserve denominators and reviewer disagreements. Do not hide a dangerous invented requirement inside a high overall completeness score.

Also record interaction burden: repeated questions, facts already available in the packet, and assistance needed. More questions are not automatically better. A concise interview can be effective when the agent has already read the evidence; it can also conceal assumptions. Inspect the trace to distinguish those cases.

Use the same answer policy for the evaluator playing the client. Do not volunteer a crucial fact in one condition and wait to be asked in another unless that difference is part of the stated design. If you repeat runs, report every attempt rather than the most convincing transcript.

Discovery and coding may diverge. The ClarifyCodeBench paper examines clarification and code generation separately. A later implementation phase should test the agreed requirements with independent scenarios, not allow the generated code to redefine success.

Our product-team discovery playbook covers using existing research and interviews. This exercise adds the narrower question of eliciting facts that are not yet in those records.

A simulated exercise can screen a workflow, but it does not establish competence with several real stakeholders, changing priorities or incomplete authority. Keep an accountable person responsible for resolving conflicts and accepting scope. The agent can organize the evidence and propose alternatives without pretending the business decision has been made.

Retain a requirement record with source reference, question or code location, confirmed/assumed/unknown status, reviewer and revision. Update it when the client changes an answer or the repository changes. Preserve the earlier conclusion so the reason for a new scope decision remains visible.

Our readiness assessment guide helps frame the business conditions before choosing a project. Our AI transformation service can connect that assessment to a bounded implementation and review process.

This article proposes the evaluation; no fictional packet was executed and no real clients were interviewed for it. A capability claim needs actual runs, an independently reviewed key and retained outcomes. Keep the published conclusion limited to the conditions you tested.