Agent Memory Is Solved. Agent Learning Isn't. — Karthik Ranganathan, Yugabyte
Karthik Ranganathan of Yugabyte discusses the critical challenge of shared learning among AI agents, arguing that while per-agent memory is solved, true collective intelligence is not. He details Yugabyte's struggles in building agentic systems, highlighting the "lost context" problem and debunking five common misconceptions about agent memory. Ranganathan introduces Meko, an agent-native persistence layer designed to provide shared, governed, and traceable context through "data packs," drastically improving RAG pipeline faithfulness while reducing context size and token costs. Heather Downing demonstrates Meko's resumability and sharability features, showcasing its ability to foster collaborative learning and capture tribal knowledge across agents.
