Agent Memory

Opening — Why this matters now For years, autonomous web agents have promised to automate the internet: booking flights, scraping dashboards, configuring enterprise tools, or simply clicking buttons so humans don’t have to. And yet, anyone who has actually tried to deploy one knows the truth—these agents fail in embarrassingly human ways. They get lost. They click the wrong thing. They forget what they were doing halfway through. ...

TL;DR Most “agent memory” is a junk drawer: it grows fast, gets noisy, and slows everything down. SEDM (Self‑Evolving Distributed Memory) proposes an auditable, efficiency‑first overhaul. It verifies each candidate memory by replaying the exact run in a Self‑Contained Execution Context (SCEC), assigns an initial utility‑aligned weight, and then self‑schedules what to retrieve next. The result: higher task accuracy with fewer tokens versus strong memory baselines on FEVER and HotpotQA. ...

What if your AI agent could remember the last time it made a mistake—and plan better this time? From Reaction to Reflection: Why Memory Matters Most language model agents today operate like goldfish—brilliant at reasoning in the moment, but forgetful. Whether navigating virtual environments, answering complex questions, or composing multi-step strategies, they often repeat past mistakes simply because they lack a memory of past episodes. That’s where the paper “Agentic Episodic Control” by Zhihan Xiong et al. introduces a critical upgrade to today’s LLM agents: a modular episodic memory system inspired by human cognition. Instead of treating each prompt as a blank slate, this framework allows agents to recall, adapt, and refine prior reasoning paths—without retraining the underlying model. ...