Retest a website readiness fix by replaying the original tasks against a frozen baseline, then comparing the changed release under the same conditions. Record what changed in the website, what changed in the task result and whether the surrounding rules still hold. Use the release record to identify what shipped and the retest evidence to decide whether the intended task works within the tested scope.
An automated audit needs human investigation when its recorded observation supports different explanations that would require different actions. A disabled control may be broken, waiting for a prerequisite or correctly restricted. The audit should preserve what it observed and escalate the unresolved cause before turning that observation into a repair instruction.
Prioritise agent readiness fixes around the customer task that must work. Contain failures that could expose information or perform an unauthorised action, then rank the remaining failures by their consequence for completing that task. Move a prerequisite ahead of the work it blocks, even when the prerequisite affects fewer routes. A long list of page defects cannot make that decision on its own.
A defensible AgenticReadiness audit report lets someone check how a finding became a recommendation. Each finding needs a precise claim, the capture supporting it, enough conditions to repeat the observation, and a reason for its priority. A readiness score cannot supply those missing connections.
Testing whether an agent can use a website requires more than checking whether it opens a page. Define a customer task, specify correct completion, and keep separate findings for discovery, comprehension and action. That separation makes a failed run useful: it identifies where the task stopped and which part of the task needs attention.
An agent readiness score can summarize which checks a system passes and help prioritize repairs. Its meaning depends on the checks, their weights and the environment assessed. A high total cannot establish that an agent can complete a particular customer task if the formula allows other strengths to compensate for that task failing.
Close an agent-readiness finding by repeating the original task against the changed page and retaining the evidence that explains the outcome. For a failed form interaction, the useful record connects the control the agent could identify, the input it submitted and the answer it returned. A change to the page is only one part of that record.
A page can load promptly and still leave an AI agent unable to finish a useful task. The distinction is what each assessment establishes. A performance report can describe loading and responsiveness. It cannot, by those measurements alone, establish that an agent selected a compatible product, checked a delivery promise and changed the basket correctly.
An AgenticReadiness audit should measure whether an assistant can complete a named customer task using the correct information, within the intended authority, and with an observable final state. The useful unit is a rule linking that task to a testable condition. A readiness score becomes useful only when its underlying rules explain what can proceed, what would fail and what remains untested.
An AgenticReadiness audit should connect every readiness judgement to a defined check, a retained observation and a condition for closing the issue. That connection makes the report useful to the engineer making a repair and the growth practitioner deciding which customer journey deserves the work. A service page that explains an offer and an enquiry action that completes successfully require different checks.