GAM: Dual-Agent Memory Solves AI Context Rot

Imagine an AI that remembers every detail from a month-long project, flawlessly recalling critical information on demand. This is now possible with GAM, a dual-agent memory architecture designed to cure AI's "context rot." Instead of just expanding memory, GAM uses a 'memorizer' to save every detail losslessly and a 'researcher' to intelligently retrieve the exact context needed, just-in-time. This approach crushed competitors on long-term memory benchmarks, achieving over 90% accuracy on the RULER test while traditional RAG systems collapsed and long-context models faltered. This signals a major shift from brute-force context windows to smarter, engineered memory systems. It’s critical for building reliable AI agents that can manage multi-day projects, track complex customer support histories, or maintain continuity in ongoing workflows.