Built: Next.js 15+ app with App Router, Tailwind CSS v4, shadcn/ui v4, Drizzle ORM + better-sqlite3, full 7-table database schema with migration, app layout shell with nav, Vercel AI SDK, and Recharts. Vitest configured with schema validation tests (21 passing).
Deviations: Used Next.js 16.1.7 (latest stable) and Tailwind CSS v4 / shadcn v4 instead of v3 — uses CSS-native config (@theme inline) rather than tailwind.config.ts.
Landmines: Prices/revenue stored in cents (integers) throughout the schema. DB defaults to ./data/local.db relative to project root. On Node.js v25, npx next build fails due to a symlink resolution bug — use node node_modules/next/dist/bin/next build as a workaround (not an issue on Node 20/22 or in deployment).
Depends on: Nothing beyond what's in the plan.
Built: Seed script (src/db/seed.ts) with 1 storefront and 20 products across 3 categories (Espresso Machines, Blenders, Cookware). Products have intentionally varying data quality: ~6 with full specs, ~8 with partial data, ~6 with minimal data. data_completeness_score computed and stored as 0-1 float. Storefront overview page at /storefront with policy summaries, categorized product grid, data quality indicators (colored dots + percentage), and product detail dialog with structured specs table and agent readability warnings.
Deviations: None.
Landmines: Seed script uses SeedProduct type to avoid TS union-type issues with varying structuredSpecs shapes. computeCompleteness uses a weighted formula (60% field presence, 40% spec depth) — not a simple ratio. shadcn v4 Dialog uses @base-ui/react under the hood (not Radix). The DialogTrigger takes a render prop instead of asChild.
Depends on: Phase 1 schema and db connection.
Built: TypeScript types for simulation output (src/lib/types.ts): ReasonCode const enum (10 codes), REASON_CODE_LABELS lookup, ReasoningStep, and AgentDecision. System prompt templates for all 6 buyer profiles in src/lib/prompts/ — each defines persona, evaluation methodology, structured output schema, 1-2 few-shot examples, and references the reason code taxonomy. Shared fragments (shared.ts) for taxonomy and output schema to avoid duplication. 6 buyer profile seed records with realistic weight distribution (Price-Sensitive: 1.4, Speed-Obsessed: 1.2, Spec-Comparator: 1.0, Brand-Loyal: 0.9, Return-Conscious: 0.8, Sustainability-First: 0.7). Dashboard page updated with BuyerProfileCards (weight bars, constraints, example mandates) and SimulationConfig (visit count selector with 25/50/100/200 options, disabled simulate button).
Deviations: None.
Landmines: Buyer profile seed data uses SeedBuyerProfile type (same pattern as SeedProduct) to avoid TS union-type inference issues when different profiles have different parameter shapes. PROFILE_PROMPTS map in src/lib/prompts/index.ts maps profile IDs (bp_001..bp_006) to prompt strings — Phase 4 will use this to look up the system prompt for each profile during simulation. Profile weights are NOT equal: they sum to 6.0 and represent relative proportions. The SimulationConfig component is client-side ("use client") because it has interactive state for visit count selection.
Depends on: Phase 2 seed script and queries.
Built: Full simulation engine: agent caller (src/lib/simulation/agent-caller.ts) using Vercel AI SDK generateObject() with @ai-sdk/anthropic, Zod 4 structured output schema, three-layer prompt (system persona + user product/storefront data + structured output), and retry-once error handling with 429 backoff. Orchestrator (src/lib/simulation/orchestrator.ts) with weighted profile distribution (profile weight / total weight * visit count), randomized product assignment (round-robin then shuffle), semaphore-based concurrency (default 8), and async generator yielding VisitResult as each completes. SSE streaming API route (POST /api/simulate) that creates a SimulationRun DB record, streams visit events with running totals, persists each AgentVisit to SQLite as it completes, and emits complete/error summary events. LiveFeed component with real-time SSE consumption, animated visit entry cards (purchase/reject/error badges), running tally bar with progress indicator, and completion summary with conversion rate and estimated revenue lost. SimulationConfig wired up with working simulate button, live feed display, and new-simulation reset. New DB queries: createSimulationRun, insertAgentVisit, updateSimulationRunTotals.
Deviations: Added zod as an explicit dependency (was already transitive via ai package). Used claude-sonnet-4-20250514 as the model — cheaper/faster for structured output generation in a demo context.
Landmines: The orchestrator generates random mandates per-profile from templates in MANDATE_TEMPLATES — these are not deterministic across runs. Visit IDs use Date.now() + random suffix (not crypto-secure, fine for demo). The SSE parser in LiveFeed manually splits on \n\n boundaries — standard EventSource API was not used because we need POST method support. Revenue lost is calculated as sum of product prices for rejected visits (in cents). The SimulationConfig defaults to 25 visits instead of 100 for faster demo iteration.
Depends on: Phase 3 buyer profiles, prompts, and types. Requires ANTHROPIC_API_KEY environment variable for Claude API calls.
Built: Aggregation engine (src/lib/simulation/aggregator.ts) that groups rejections by reason_code, computes revenue impact (rejection count * average product price), ranks clusters by impact descending, and persists RejectionCluster records and simulation-level summary stats to the database. API routes for aggregation (POST /api/aggregate) and dashboard data (GET /api/dashboard?runId=xxx). Rejection dashboard UI (src/components/dashboard/RejectionDashboard.tsx) with summary bar (conversion rate, total rejections, estimated revenue lost), Recharts bar chart of rejection counts by reason code, ranked cluster cards with expandable drill-down to individual rejections, profile badges, and action recommendations. Revenue impact tooltip (RevenueTooltip.tsx) shows the calculation formula on hover (e.g. "38 rejections x $479 avg price = $18,202"). Main page refactored into MainDashboard client component with tabbed navigation (Simulation Feed / Rejection Dashboard) that auto-transitions to dashboard on simulation completion. LiveFeed.onComplete and SimulationConfig.onSimulationComplete callbacks updated to pass runId. New DB queries: getSimulationRun, getLatestCompletedSimulationRun, getRejectionClustersByRun, getAgentVisitsByRun, getAgentVisitsByRunAndReasonCode, getProduct, getBuyerProfile. 12 new aggregation tests (clustering, revenue calculation, ranking, conversion rate, edge cases) — all 69 tests passing.
Deviations: None.
Landmines: The aggregation engine is triggered client-side after simulation completes (POST to /api/aggregate) rather than server-side in the SSE stream — this keeps the simulation stream simple and makes aggregation idempotent (can re-aggregate). Revenue impact uses rejection count * average price of affected products (not sum of individual prices), which gives a per-cluster estimate. The RECOMMENDATIONS map in aggregator.ts provides static recommendation text per reason code with an estimatedRecovery of 60% of revenue impact. The MainDashboard is a client component ("use client") because it manages tab state and runId; the parent page.tsx remains a server component that fetches profiles. Cluster IDs and ranks are ephemeral — re-aggregation deletes and re-creates clusters.
Depends on: Phase 4 simulation engine, agent visits data, and SSE completion event with runId.
Built: ReasoningTrace component (src/components/dashboard/ReasoningTrace.tsx) that renders a vertical timeline/stepper of the agent's evaluation chain. Displays the agent's mandate at top, numbered steps with action description, data evaluated, and finding/outcome. Final decision step is visually highlighted with colored border and dot (red for reject, green for purchase). Rejection decisions include a "Blocking constraint" callout with the reason code in plain language. Integrated trace expansion into two locations: (1) LiveFeed visit rows — each entry has a "View trace" toggle that expands inline; (2) ClusterCard rejection drill-down rows — each individual rejection has a "View trace" toggle. Updated VisitEvent interface in LiveFeed to include reasoningTrace (was already in the SSE payload from the orchestrator, just not consumed). Updated AgentVisit interfaces in RejectionDashboard and ClusterCard to include reasoningTrace (was already returned from the DB query via JSON column). All 69 tests passing.
Deviations: Did not use the shadcn Collapsible component — used simple useState toggle with conditional rendering instead, consistent with how the existing ClusterCard handles its expand/collapse. The Collapsible component from base-ui adds animation overhead that isn't needed for a demo.
Landmines: The reasoning trace data was already flowing end-to-end (agent caller -> orchestrator -> SSE stream -> DB storage -> dashboard API) but was not being rendered in the UI until this phase. The reasoningTrace field in the SSE payload uses camelCase (dataEvaluated) matching the ReasoningStep TypeScript type, while the Zod schema in agent-caller.ts uses snake_case (data_evaluated) and maps it during parsing. The trace toggle uses local useState in each row component — expanding a trace does not affect other rows.
Depends on: Phase 4 simulation engine (SSE payload with reasoningTrace), Phase 5 dashboard (cluster drill-down with individual rejection rows).
Built: Full recommendation-to-action pipeline. Deterministic recommender (src/lib/simulation/recommender.ts) maps each reason code to a specific action type and recommendation template with 60% estimated recovery. Storefront mutation API (PATCH /api/storefront) with apply and undo handlers, plus GET /api/storefront?runId=xxx to retrieve applied actions. Six action handlers implemented: add_expedited_shipping (adds 1-2 day option), structure_return_policy (converts free-text to structured fields), add_sustainability_certs (adds Energy Star/Fair Trade/FSC), enrich_product_specs (fills in category-specific default specs, boosts completeness score), reduce_price (10% reduction on affected products), add_stock_status (marks out-of-stock as in_stock). Action preview modal (src/components/actions/ActionPreview.tsx) shows before/after diff, action type badge, estimated recovery, and confirm/cancel buttons. Uses shadcn Dialog with render prop pattern. ClusterCard updated with "Apply Fix" button that opens the preview modal, "Fix Applied" badge with green border after applying, undo button that reverts changes via the stored changePreview.before state, and estimated recovery display. RejectionDashboard tracks applied actions in state, loads existing actions on mount to restore applied state across tab switches, and shows a recovery summary card when fixes are applied. New DB queries: updateStorefrontPolicies, updateProduct, getProductsByIds, createStorefrontAction, getStorefrontActionsByRun, getStorefrontAction, updateStorefrontAction, getRejectionCluster. All 69 tests passing.
Deviations: Used deterministic mapping only (no Haiku LLM call) as specified in the plan for MVP. The recommender in recommender.ts has richer action-specific templates than the simpler RECOMMENDATIONS map in aggregator.ts — the aggregator's map is still used during cluster creation, while the recommender's getActionType() function is used to map reason codes to action types at apply time.
Landmines: Undo relies on the changePreview.before state stored in the StorefrontAction record — if the storefront is modified externally between apply and undo, the revert may produce inconsistent state. Cluster IDs are ephemeral (re-aggregation deletes/recreates them), so applied actions tied to old cluster IDs will lose their association after re-aggregation. The ActionPreview component uses shadcn Dialog's render prop on DialogTrigger (not asChild — base-ui pattern). Product spec enrichment uses category-specific defaults (Espresso Machines, Blenders, Cookware) — products in unknown categories get minimal defaults. The appliedActions state in RejectionDashboard is restored from the DB on mount, but since cluster IDs change on re-aggregation, applied actions from a previous aggregation pass won't show as applied.
Depends on: Phase 5 rejection clusters and dashboard, Phase 2 storefront/product seed data.
Built: Full before/after comparison loop closing the optimization workflow. Comparison engine (src/lib/simulation/comparator.ts) takes two simulation runs and computes: aggregate deltas (conversion rate change in percentage points, total rejection delta, revenue recovered), per-cluster deltas (each reason code shows before/after count and revenue impact change), and "flipped" visits (same profile + same product, different outcome) with divergence point detection in the reasoning traces. Comparison API (GET /api/compare?runId=xxx) fetches both runs via previousRunId linkage and returns full ComparisonResult. BeforeAfterComparison component shows a summary delta bar (conversion rate change, rejection delta, revenue recovered), per-cluster delta rows with count and revenue changes, and a flipped visits section with expandable trace comparisons. ComparisonBarChart renders a side-by-side Recharts grouped bar chart (before/after rejection counts by reason code). TraceComparison shows both traces side-by-side for flipped visits, highlighting the divergence point where outcomes diverged. Simulate route (POST /api/simulate) updated to capture full storefront snapshot (all products + policies as JSON) and link runs via previousRunId (defaults to most recent completed run). Re-run flow: RejectionDashboard shows "Re-run Simulation" button after fixes are applied, MainDashboard passes rerunConfig (previousRunId + visitCount) to SimulationConfig, which auto-starts a re-run. LiveFeed passes previousRunId through to the simulate API. After re-run completes, dashboard loads with before/after comparison visible at top. All 69 tests passing.
Deviations: None. Followed the plan specification exactly.
Landmines: The previousRunId defaults to getLatestCompletedSimulationRun() if not explicitly provided — this means any new simulation will auto-compare against the most recent completed run. The comparison will return 404 with hasPrevious: false if the run has no previousRunId; the BeforeAfterComparison component handles this gracefully by rendering nothing. Flipped visit matching uses profile ID + product ID as the key — since mandates are randomly generated and products are shuffled, the match is approximate (same profile type evaluating same product, not necessarily the exact same agent scenario). The divergence step detection compares trace step outcomes sequentially; if agents take different-length paths, it falls back to the end of the shorter trace. The rerunConfig auto-start in SimulationConfig uses a ref to prevent double-triggers when React re-renders.
Depends on: Phase 7 actions/recommendations (the re-run button appears after fixes are applied), Phase 5 rejection clusters and dashboard, Phase 4 simulation engine.
Built: Indigo primary color replacing achromatic gray; diversified 5-color chart palette; hero context banner with "Agentic Commerce" pill and product narrative; buyer profiles moved above simulation config; metric cards get semantic icons (TrendingUp/XCircle/TrendingDown) with green/red/amber colors; skeleton loading state replacing single pulsing text; inviting empty dashboard state with inline tab link; bot/agent icon in header; broken /simulations nav link removed.
Deviations: None.
Landmines: The new indigo primary (oklch(0.45 0.22 265)) affects every component that uses bg-primary, text-primary, border-primary, or ring — visually verify buttons, focus rings, badges, and active states look correct together. The Badge import in MainDashboard.tsx is unused (pre-existing dead import) — harmless but will trigger lint warnings if strict mode is enabled.
Depends on: Nothing beyond what's in the codebase — pure UI changes.