The Agent Loop: How AI Goes From Answering Questions to Doing Things

A structured walkthrough of how AI systems evolve from single LLM calls to full agent loops. Covers the augmented LLM as a foundational unit, workflow patterns (prompt chaining, routing, parallelization, orchestrator-worker), and the agent loop itself with its four steps: perceive, reason, act, observe. Explains the four decision branches a model can take each turn, the ReAct prompting pattern, guardrail placement at loop boundaries, and three key tradeoffs: compounding error rates across steps, scaffolding requirements, and when a simpler workflow is the better choice.