Multi-Step AI Agents: What They Are & How They Work

This post details the mechanics of multi-step AI agents, explaining the think-act-observe loop (ReAct pattern) and the challenges of managing state in production. It highlights how context issues like poisoning, distraction, confusion, and clash can lead to agent failures. The post emphasizes the critical role of the data layer in providing short-term memory, long-term memory, and operational state for reliable agent execution, and how Redis Iris addresses these needs.