Building reliable AI agents requires more than a good model — it demands a carefully layered loop architecture. Four loops are described: (1) the core agent loop where an LLM calls tools until a task is done, (2) a verification loop that grades outputs against a rubric and sends feedback, (3) an event-driven loop that connects agents to ecosystems via webhooks and cron triggers, and (4) a hill-climbing loop that analyzes traces to continuously improve the agent's prompts, tools, and configuration. LangChain primitives like RubricMiddleware, LangSmith Deployment, Fleet, and Engine are mapped to each loop level. Human oversight is positioned as a first-class concern at every level, not an afterthought. The key insight is that value compounds most in loops 3 and 4, where agents are embedded in ecosystems and self-improve over time.
