agent memory

Agent Memory Forgetting Study Reveals Control-Plane Trade-offs for Enterprise AI Systems

A new architectural study of AI agent memory, based on 13 system configurations and a 385-case adversarial benchmark, reveals that forgetting failures—not recall failures—are the dominant cause of production errors. The research introduces ForgetEval, a benchmark for evaluating forgetting, and an Adapter Protocol for integrating heterogeneous memory stores. Three placement regimes for LLM intervention are compared, with a mutation-time hook achieving 91.7-93.2% overall accuracy at $0.17 per run.