Rethinking Agent Memory: From Raw Ledgers to Non‑Parametric Systems
This article analyses the nature of memory for LLM‑based agents, arguing that memory is a closed‑loop system composed of a raw ledger, derived views, and a policy layer, and explores how non‑parametric designs, system‑2 architectures, temporal structuring, and skill‑based execution can bridge the gap between parametric and non‑parametric memory while highlighting key bottlenecks and practical design guidelines.
