The request is not yet design-ready because it lacks a defined failure, affected user journey, supported decision, target population, and measurable outcome. The architect should therefore facilitate structured discovery rather than prematurely selecting an AI pattern or model.
Discovery should identify where the current customer journey breaks down, who experiences the problem, what decision or task requires assistance, what data is available, and which metric would demonstrate improvement. The team can then establish baseline performance, target thresholds, operational constraints, risks, and explicit non-goals. Anthropic’s evaluation guidance states that an LLM application should begin with clearly defined success criteria and a method for measuring them. Define Success Criteria and Build Evaluations
Option A abandons the sponsor instead of helping translate a business concern into an actionable problem. Option B risks producing a generic demonstration unrelated to a validated customer need. Option C substitutes the architect’s assumptions for stakeholder evidence and may commit the organization to the wrong scope.
Competent delegation begins by defining the decision, intended outcome, authority boundaries, and target metric before determining which responsibilities should be assigned to Claude, conventional software, or human reviewers.
Study Guide references/topics: Structured discovery; delegation competency; problem definition; target metrics; decision support; measurable business outcomes.
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