01 / AI ENGINEERING

AI Engineering

Foundation

Production note
AI Engineering: principles from production
The principles Cleon applies to every AI build — ground before you generate, evaluate before you ship, govern every action, and never confuse a demo with a product. The discipline that turns a capable model into a system that survives Monday morning.

By topic

Agents & Orchestration
8 articles
Building agents that ship, not demo: anatomy, orchestration patterns (single-loop to LangGraph graphs), tools and actions, and the production discipline — composed across Agentforce, LangGraph, Claude, and MCP.
Grounding & Retrieval
8 articles
Connecting models to your knowledge: RAG done right — chunking, embeddings, retrieval quality — across Agentforce retrievers over Data 360 and external vector stores. The foundation an agent’s answers stand on.
Prompting & Context Engineering
8 articles
Steering models reliably: system prompts, instructions, context windows, structured output, and prompt caching. The craft that turns a capable model into a dependable component.
Evaluation & Observability
8 articles
Knowing it works — and stays working: evals, test sets, LLM-as-judge, tracing, regression, and the monitoring that catches a silent degrade before a customer does.
Production & Governance
8 articles
Shipping and operating AI: cost, latency, guardrails, PII and safety, human-in-the-loop, accountability, and deployment. The gap between a demo and an AI that runs on Monday morning.