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

What this post added

This post introduces multi-step AI agents, explaining their think-act-observe loop and the challenges they face in production, particularly concerning state management and context handling. It details how these agents break down goals into sequential actions, manage accumulating context, and deal with error propagation. The post highlights the need for a robust data layer to support short-term memory, long-term memory, and operational state, and introduces Redis Iris as a solution for providing agents with fresh context and long-term memory. It discusses specific failure modes like context poisoning, distraction, confusion, and clash, and emphasizes that structural fixes in the data layer are crucial for agent reliability.