What problem does it solve? Designing production AI agent SaaS platforms involves complex decisions around monorepo structure, multi-platform deployment, sandboxed agent execution, multi-database architecture, and LLM provider integration, and this Skill provides proven patterns for each. ## Core Features & Use Cases - Monorepo Architecture: pnpm/npm workspace organization with Turborepo, separating apps, shared packages, backend, and infrastructure. - Agent Runtime Patterns: Sandbox isolation via Docker, Daytona, E2B, or V8 isolates, with stateless, stateful, and durable agent execution models. - Multi-Database & LLM Integration: PostgreSQL + graph database + Redis data flows, plus multi-provider LLM strategies with failover and streaming via SSE or WebSocket. - Use Case: When building an AI agent chat platform that must run on web and mobile, execute agent code in isolated sandboxes, and stream responses from multiple LLM providers, apply these patterns to structure the codebase and data layer correctly from day one. ## Quick Start Ask the AI to design a monorepo architecture for a multi-platform AI agent chat SaaS with sandboxed agent execution and multi-database support.
