Capstone topic for the AI engineering discipline. An AI product is not 'an LLM call' — it's a seven-layer system where most production failures happen outside the model. Covers: the seven-layer architecture (frontend, orchestration, retrieval, guardrails, model, output filter, telemetry); observability and distributed tracing for multi-step agents; explicit vs implicit user feedback; the feedback flywheel from telemetry to eval set to improvement; A/B testing under output stochasticity (compare distributions, not means); shadow vs canary deployment; human-in-the-loop patterns and confidence-threshold escalation. The framework that turns 'we built a demo' into 'we shipped and continuously improve a product.'
