Research Question

Which UI / information-architecture patterns best let a solo builder confirm (Accept) and recall (catalog) architecture decisions, completing the decision-record lifecycle?

Focus on the confirm + catalog stages after drafting and AI review. NOT the drafting / AI-review screens, and NOT the review algorithm.

Context

We run a Decision Pipeline producing Architecture Decision Records (ADRs): a solo developer drafts a decision, an AI pipeline reviews it (triage → blind-spot → scoring → cross-validation), and the goal is an Accepted, audit-trailed ADR. Volume 5–15 ADRs/month.

The current web UI only supports drafting and watching the AI review. It has no screen to (a) confirm / Accept a decision (status Proposed→Accepted with an audit trail: who, when, on what basis) or (b) browse / recall the corpus (a catalog showing status, review score, re-submission count, stuck items). Acceptance now happens ad-hoc outside the tool, and there is no in-tool way to find a past decision or detect that a new one contradicts an existing one.

Two constraints dominate: a single operator (n=1) with audit needs, and minimizing cognitive load ("where is it / which do I pick" friction). A recurring failure mode: the AI reviewer over-reviews (rejecting sound decisions in a non-converging loop); the human must accept anyway, but no UI surfaces that state or records the override + rationale.

Questions

1. Confirm / Accept UI: How do ADR / decision tools (Log4brains, adr-tools, adr-kit, Backstage ADR, MADR, Zimmermann) and lightweight approval flows handle sign-off and its audit trail (who/when/basis, status transition)? Comparison matrix with primary-source URLs.
2. Catalog / recall UI: How do decision catalogs and trackers (Linear, Notion, GitHub Projects, Jira) present a corpus for browse, filter, stuck-item detection, and finding related / contradicting items, with minimal cognitive load for one person?
3. MVP for n=1: Which confirm + catalog elements are must-have vs deferrable for a single audit-bound operator? Give must / should / nice with rationale.
4. Information architecture: What state model (draft / run / record) and screen transitions move an item across draft → review → decide → catalog? Provide a state × screen mapping.
5. Surfacing AI review: How do AI-review UIs (Graphite, CodeRabbit, Devin, Cursor) present results, rejections, and non-convergence, and what escalation / accept-with-rationale override patterns exist?

Output

• Executive summary (3–5 findings; recommended confirm + catalog UI for a solo builder)
• Per-question analysis with primary-source URLs
• Comparison matrices (confirm-flow tools; catalog affordances)
• Must / should / nice MVP table (n=1)
• draft → review → decide → catalog IA recommendation (state × screen)
• Open risks / further research