Why Your LLM Agent Forgot What It Did 5 Steps Ago

Short summary

LLM agents lose context mid-task because model attention degrades across massive token windows—stacking more tokens adds noise rather than clarity. Traditional solutions (RAG, custom memory layers) are brittle and expensive. The author reframes LLM memory as an infrastructure problem and introduces ICE, an emerging memory manager optimizing KV-cache hits and enabling agents to maintain coherence across 100B+ token horizons.

• •Context degradation in multi-step agent tasks stems from attention limits in large token windows, not insufficient context size
• •DIY memory stacks (RAG, summarization loops, custom caches) are brittle, costly, and pose cross-tenant security risks
• •Infrastructure-level solution (ICE) applies OS virtual memory principles to LLM context, with KV-cache optimization and PostgreSQL RLS isolation

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