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

One agent can remember. A team of agents still can't learn together. Karthik Ranganathan, co-founder and co-CEO of Yugabyte, explains why per-agent memory is a solved problem but shared learning across agents isn't. When one agent hands work to another, it passes on the output but loses the reasoning, the dead ends and the context, so the next agent burns tokens re-deriving it all. He covers five misconceptions about memory, what Yugabyte got wrong building its own agents, and how fixing its RAG pipeline one variable at a time took faithfulness from 14% to 82% with about one-seventh the context. Then Heather Downing demos Meko, Yugabyte's agent-native persistence layer, resuming sessions across agents and promoting private memories into shared data packs. In this talk: • Why more memory, shared files and bigger context windows aren't learning • Shared context that's governed, promotable and traceable to its source • How tuning a RAG pipeline cut context size about 7x while raising precision • Demo: resuming and sharing context across Claude, Codex and other agents SPEAKERS Karthik Ranganathan, Co-founder & co-CEO, Yugabyte LinkedIn: https://www.linkedin.com/in/kranganathan X: https://x.com/karthikr Heather Downing, Developer Advocate, Yugabyte LinkedIn: https://www.linkedin.com/in/heathermdowning/ X: https://x.com/quorralyne LINKS Meko: https://mekodata.ai/ Meko docs: https://docs.mekodata.ai/ Meko skills (GitHub): https://github.com/yugabyte/meko-skills Yugabyte: https://www.yugabyte.com/ CHAPTERS 0:00 Intro 0:12 Memory is solved, learning isn't 0:47 Learning happens in groups 1:17 Why a database company built this 2:02 A year of failing to ship agents 2:37 The real failure: lost context 3:27 The new-teammate problem 3:57 Five misconceptions 5:02 Shared, governed, traceable context 5:42 What we got wrong 6:37 Fixing RAG: 14% → 82% faithfulness 7:27 Introducing Meko and data packs 8:36 This deck was built with Meko 10:20 Demo: collective knowledge 11:25 Sharing context saves tokens 12:20 Demo: saving to a data pack 13:25 Demo: resuming in a fresh session 14:20 Demo: sharing with another agent 14:55 Private vs shared memory 16:15 Proving the token savings 16:45 Human promotion as a training signal 17:52 How the team uses Meko 19:26 Capturing tribal knowledge 19:40 Try Meko Recorded at the AI Engineer World's Fair 2026 in San Francisco. Subscribe for more talks from the engineers building with AI. AI Engineer: https://ai.engineer YouTube: https://www.youtube.com/@aiDotEngineer X: https://x.com/aiDotEngineer LinkedIn: https://www.linkedin.com/company/aidotengineer/ #AgentMemory #AIAgents #AIEngineer