Step 5 of 7: From Idea to Impact
The instinct is to build the full agentic workflow before shipping anything. The right move is almost always the opposite: find the thinnest version of the workflow that's reliable enough to trust, ship that, and expand scope only after real usage proves the foundation holds.
Rules-based v1: before any model call, can a deterministic system handle the top 20% most common cases? For disputes: duplicate-charge detection is often a rules problem (same merchant, same amount, within 48 hours), not an AI problem. Ship this first; it needs no eval infrastructure and builds the surrounding workflow you'll reuse later.
Data collection: instrument the rules-based version to log every case it doesn't handle, with enough context to eventually train or prompt against. This phase is invisible to users but is what makes v2 possible.
AI v2: narrow scope, the single dispute sub-category with the most collected examples and clearest reasoning pattern. Ship with confidence-tiered UX.
Agentic v3: only after v2's edit/accept rates prove the model's judgment is reliable within its narrow scope.
Promotion signal between stages: not a calendar date, a data threshold. Move from v1 to v2 when you've logged 200+ instrumented cases in the target category. Move from v2 to v3 when edit rate on v2 drafts has been stable under 30% for 4 consecutive weeks.
2. Claude Code Workflow for AI Feature Builds: the 9-skill loop with the one addition specific to AI features that most teams skip.
3. Build Readiness Checklist: the actual gates, including the p95 latency and fallback-path tests most teams forget.
4. Worked Example: how a 2-week rules-based v1 freed up enough capacity that leadership stopped pressuring for a rushed v2.
5. Common Pitfalls: why promoting to the next build stage on a calendar deadline instead of a data signal is how unreliable features ship.
6. Nuance Most Guides Skip: what "MVP" actually means for AI features, and why it's not the same MVP you learned for regular software.