Production AI systems extend far beyond the model itself, requiring a layered architecture integrating data, application logic, security, and infrastructure. SotaTek emphasizes designing systems around business workflows rather than starting with model selection. A typical production AI architecture includes: User/Business Workflow → Application → Authentication/Authorization → AI Orchestration Layer → Retrieval Tools/Knowledge Sources → Model → Guardrails/Validation → Response → Logging/Evaluation → Monitoring/Feedback. Critical components include authorization-aware retrieval in RAG systems (e.g., filtering documents by tenant_id and roles) and AI agents with permissions, audit logs, idempotency, and failure handling. Model selection involves trade-offs among accuracy, latency, cost, and context length, with routing layers directing tasks to appropriate models. Evaluation pipelines track metrics like retrieval precision, hallucination rates, and cost per request, while observability monitors model latency, token usage, and tool execution. Guardrails are implemented at multiple layers (input validation, output filtering, tool authorization) to enforce security. Deployment choices (cloud, private, edge) depend on latency, data sensitivity, and compliance. The article underscores that successful AI production systems require systems engineering principles, treating AI as one component within a broader, reliable, and observable infrastructure.