The article surveys recent LLM‑based agent memory research, presenting a unified framework that breaks memory systems into four components, detailing their design choices, experimental evaluation on LOCOMO and LONGMEMEVAL, key findings, and a new low‑token SOTA architecture.
GLM-5.1, the newly open‑sourced large language model, leads global code‑generation benchmarks, excels at eight‑hour continuous long‑term tasks, can build a complete Linux desktop in eight hours, and even creates a short video from an article with just one prompt.
The article explains that AI struggles with extended, complex tasks not because models lack intelligence but due to missing systematic engineering practices, and proposes a Harness Engineering framework that introduces external memory, task decomposition, fixed SOP loops, and test‑driven safeguards to turn AI agents into reliable, production‑grade collaborators.
