6.5 — Supporting the Lifecycle Phases
CCAR-P Domain 6 · Task 6.5
What You Need to Know
Phases are gated by outputs, not dates. The architect’s job does not end at deployment.
Phase Outputs
| Phase | Must produce |
|---|---|
| Discovery | Measured baseline, agreed success criteria, data/compliance assessment, named owner |
| Design | Architecture + decision records, evaluation plan, oversight/control design, service-level envelope |
| Build & validate | System + measured performance against agreed criteria (demo ≠ measurement) |
| Handoff | Transferred ownership (more than transferred code) |
| Operate & monitor | Trend against criteria; working incident loop |
| Iterate | Evidence-gated changes that re-enter design |
Ownership Transfer = Capability Transfer
Receiving team must be able to:
- Run evaluations and get a comparable answer
- Read a trace well enough to localise a failure
- Roll a change back
- Know who to reach when none of that is enough
Missing any → operate but refuse to change (paralysis mistaken for stability).
Iteration Is Evidence-Gated
Prompt edits against last week’s complaints without a regression suite = each edit fixes the reported case and silently moves an unreported one.
Certain Events
Model deprecation, corpus migrations, connector/dependency changes, regulatory change, business-process redesign. Plan for them in design. On deprecation notice: test replacement early, audit usage by API key/model, re-benchmark cost and latency.
Quiet is not evidence. A system with no complaints, a baseline nobody has re-run, and a moved corpus is unevidenced, not healthy.
Going Backwards Is Professional
When production evidence contradicts a discovery assumption, return to discovery. Carrying on because the plan says so buys a delivery date and loses the outcome.
Exam Traps
- Reading a quiet, complaint-free system as healthy and waiting for drift to be reported
- Handing a model deprecation to the commercial/vendor-relationship owner
- Shipping prompt edits against last week’s complaints and calling it iteration
- Continuing to build against a discovery assumption that production evidence has already contradicted
Check your understanding
Practice Scenario
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Sources
- Define success criteria and build evaluations, Claude Platform Docs
- Demystifying evals for AI agents, Anthropic
- Model deprecations, Claude Platform Docs