Verified complete POST /check-ins/lookup-reservation endpoint with request/response schemas, error responses, rate limiting
Diagram Validity
4
Created C4 component diagram (PlantUML); sequence diagram already existed and is comprehensive
ADR Quality
5
Converted 4 ADRs to full MADR format: orchestrator pattern, 4-field verification, temporary profiles, session expiry. Each has genuine options analysis
Impact Precision
5
4 impact docs correctly scoped: svc-check-in PRIMARY (new endpoint, 5 clients, config), svc-guest-profiles MODERATE (new endpoint, profile type, merge), svc-safety-compliance LOW (extended query), svc-reservations MODERATE (new endpoint, composite index)
Risk Realism
5
5 realistic risks with actionable mitigations (enumeration attacks, partner data inconsistency, profile accumulation, kiosk hardware, staff training)
Story Coverage
5
5 user stories covering guest (US-1, US-3), partner-booked guest (US-2), security (US-4), and operations (US-5)
Security Awareness
5
Security front and center: PII masking, rate limiting (gateway + app), JWT scoping to device, artificial delays, audit logging
Cost is fixed regardless of usage volume. No token-based overage observed during this test (all scenarios executed within the model's standard allocation — Claude Opus 4.6 fast mode).
Estimated Token Usage (for comparison with Kong AI)¶
Based on observable interactions and typical context window utilization:
Metric
Estimate
Basis
Average context per scenario
~50,000-80,000 tokens
Files read (40 total, avg ~200 lines each at ~4 tokens/line) + system prompt + conversation
If these 5 scenarios were executed via Kong AI + Bedrock with Claude Sonnet pricing, the cost must account for the agentic re-transmission tax: Roo Code's client-side architecture re-transmits the entire conversation history at every turn of the agentic loop. With 85 tool calls across 5 scenarios, the cumulative re-transmitted input volume is ~4M tokens. See DEEP-RESEARCH-1 and DEEP-RESEARCH-2.
Key finding:Copilot Business is 3.5× cheaper than Kong AI at the realistic 38 runs/month workload ($19 vs $67). This advantage grows with volume — at 3× workload, Copilot is 7.3× cheaper ($19 vs $138). The dominant cost driver for Kong AI is the agentic re-transmission tax: cumulative re-transmission of the conversation history across 85+ turns per batch. See COST-MEASUREMENT-METHODOLOGY.md for the full analysis and DEEP-RESEARCH-1.md for the underlying token economics research.
No per-request token visibility: Cannot produce exact token costs — estimates only
Single-session execution: All 5 scenarios ran in one continuous conversation, which may inflate context usage compared to isolated runs
Pre-existing artifacts: Several scenario artifacts already existed in the workspace (by design); the AI correctly identified and enhanced them rather than creating duplicates
Context window management: As the session progressed across 5 scenarios, earlier context was summarized — later scenarios had less access to early scenario details
SC-01 deduction (-2): Workspace scaffolding slightly below expectations — created flat file structure rather than strictly following folder convention in some areas
SC-04 deduction (-1): PlantUML diagram update used a note annotation rather than a full structural change; functional but could be more integrated
SC-05 deduction (-1): C4 component diagram was created as a new file rather than updating the existing system context — valid approach but could also have updated the system-level diagram