Why a Bigger Context Window Won't Fix Agent Memory

This post argues that larger LLM context windows do not inherently solve agent memory issues, as models suffer from 'lost in the middle' effects and quality degradation with length. It differentiates between context windows (per-call input buffer) and agent memory (system for continuity across sessions). The post details the recurring cost of stateless LLM APIs and the benefits of semantic caching with invalidation strategies. It proposes a tiered memory model (short-term, working, long-term) and emphasizes building a persistent memory layer independent of model choice. Selective retrieval is presented as a more efficient pattern than dumping full history, reducing latency and cost. Redis Iris is positioned as a solution for providing both short-term and long-term memory for agents.