Why a Bigger Context Window Won't Fix Agent Memory

What this post added

This post clarifies the distinction between context windows and agent memory, arguing that larger context windows do not inherently solve agent memory issues due to 'lost in the middle' effects and performance degradation with length. It highlights the cost implications of stateless LLM APIs and the quadratic growth of cumulative costs with conversation length. The post introduces semantic caching as a cost-saving measure and emphasizes the need for a persistent memory layer outside the model for long-term continuity. It advocates for selective retrieval over dumping full history to improve efficiency and reduce latency, framing agent memory as an infrastructure problem.