Agent Memory Is Solved. Agent Learning Isn't. — Karthik Ranganathan, Yugabyte

This post contributes to the field of agent memory and harness engineering by highlighting the limitations of current agent architectures where context and reasoning are lost when passing work between agents. It introduces the concept of 'agent learning' as distinct from 'agent memory,' arguing that true learning requires shared, governed, and traceable context. The talk details how tuning a RAG pipeline improved faithfulness from 14% to 82% while reducing context size by 7x. It introduces Meko, an agent-native persistence layer, which enables resuming sessions across different agents (Claude, Codex) and promotes private memories into shared 'data packs.' This system aims to capture tribal knowledge, reduce token waste, and facilitate collective learning among AI agents.