High-Value If, Low-Value Foreach and the Engineering Logic Behind Reliable AI Agents

This article presents a detailed framework for designing reliable AI agent systems around the separation between stochastic judgment and deterministic execution. Rather than treating LLMs as universal executors, it argues that models should only appear at “high-value ifs” — decision points involving uncertainty, semantic complexity, risk, or trade-offs — while repetitive “foreach” execution is delegated to workflows, tools, schemas, and deterministic systems. The piece also explores concepts lik...