Oct 05, 2026

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.

Oct 02, 2026

Your Coding Agent Is 6 Months Out of Date — Jakub Hojsan, Exa

Jakub Hojsan, a forward deployed engineer at Exa, explains how search built for coding and code-review agents closes the gap left by model knowledge cutoffs. He covers why agents need explicit rules for when to search, how query-dependent highlights cut a 100,000-character page to about 500 characters without added latency, and how Exa compares with native web search on transparency, cost, and model-provider independence, with a preview of Exa Agent.

Sep 25, 2026

Why LLM Recommenders Will Be AI's Biggest Consumer App — Devansh Tandon, Meta

Devansh Tandon, who leads Meta Recommendations Research, argues that recommendation systems scale like LLMs, that the field is early on that curve, and that LLM recommenders will become one of AI's biggest consumer applications. He walks through four S-curves of recommendation, a three-step recipe built on semantic IDs, and why content feeds can be up to 100x cheaper per hour of engagement than AI chat apps.

Jul 22, 2026

CrabRAG: Why Automated Assistants Need Graph Memory, Not More Tokens — Stephen Chin, Neo4j

Stephen Chin demonstrates how current LLM agent memory systems, relying on markdown files or vector databases, lead to token inefficiency, hallucinations, and a lack of multi-hop reasoning. He showcases graph databases as a superior alternative, providing precise, explainable, and auditable answers for complex, large-scale problems through a live demo of a home lab digital twin.

Jul 13, 2026

The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO

Engram co-founder Dan Biderman discusses building AI that learns from users, critiquing long context, RAG, and compaction's limitations. He introduces Engram's approach of compressing knowledge into "cartridges" and model weights via continual learning and gradient-based updates, aiming for "intuition" over retrieval. The vision extends to personal, "Tamagotchi" AI models and addresses the critical need for token efficiency and "doing more with less" in both enterprise and personal AI.

Jun 29, 2026

The Future Is Domain-Specific Agents - Justin Schroeder, StandardAgents

Justin Schroeder argues for a paradigm shift in AI agent development from monolithic, context-inflated agents (inheritance) to modular, domain-specific agents (DSAs) that operate through composition. He explains how DSAs offer superior token efficiency, cost savings with smaller models, enhanced security through capability limits, and better scalability, predicting their widespread adoption by 2027 as a solution to rising AI costs and the need for practical, customer-facing AI.