Executive Summary
Production-grade platform for creating, testing, versioning, and executing user-defined AI agent skills with strict permission controls, human-in-the-loop approvals, per-user multi-tenant data isolation, and structured execution logging.
The Operational Friction
Executing user-defined AI skills introduces security risks (unbounded API calls, mutating database actions without approval), tenant data leakage in multi-user environments, and non-reproducible skill executions due to unversioned mutations.
System Architecture & Engineering Solution
Architected a Clean Architecture platform with Next.js 16 (App Router) and FastAPI. Engineered a bounded execution engine with step limits and retry capping (max 3), per-user multi-tenant data isolation, human-in-the-loop approval workflows with idempotency protection for mutating tools, immutable versioning (Draft -> Published v1 -> v2 draft bump), Supabase JWT authentication, Gemini LLM sample input generation, and structured logging (structlog).
Measurable Impact & Production Results
- 100% per-user data isolation with composite (user_id, name) uniqueness across Supabase Auth & PostgreSQL
- Bounded execution engine enforcing max step constraints, tool permission validation, and 3-retry resilience
- Human-in-the-Loop approval state machine with idempotency protection for mutating operations
- Immutable versioning lifecycle preserving historical execution reproducibility (Draft -> Published v1)
- Comprehensive 111-test suite with pytest covering unit, integration, permission, and approval workflows