Oct 22, 2026

The Recommendation Engine for Commodity Traders — Anekha Sokhal, Moshi

Anekha Sokhal, founder and CEO of Moshi, explains how her background in energy derivatives trading and machine learning research led to a real-time intelligence platform for financial traders. The discussion covers how Moshi structures public, alternative, and private data into a connected graph, why recommendation and UI design matter as much as modeling, how trusted sources and conflicting reports are handled, and the pressure on AI engineers to become the whole team.

Oct 08, 2026

Turning Agent Memory Into Skills That Work — Will Lyon, Neo4j

Neo4j's Will Lyon argues that embedding-based retrieval gives agents recall but not actionable knowledge. He presents a context graph model of agent memory with short-term, long-term, and reasoning memory, then shows how to distill grounded, governed skills from that graph using APE typed execution graphs, with grounding, coverage, and coherence checks, staleness alerts, and a live demo of the Neo4j Agent Memory Service building a patient-intake skill.

Jul 23, 2026

AI on Your Lakehouse: Context Comes in Shapes, Not Queries — Zach Blumenfeld, Neo4j

AI agents often struggle with context in lakehouses, leading to confident but incorrect answers. This workshop by Zach Blumenfeld introduces three Neo4j graph shapes built on lakehouse data—Connections (semantic layer), Trees (document outlines), and Communities (themes)—to provide essential context, enabling agents to accurately navigate structured and unstructured data, answer complex estate-level questions, and overcome limitations of traditional Text2SQL and vector search.

Jul 23, 2026

Citation Needed: Provenance for LLM-Built Knowledge Graphs — Daniel Chalef, Zep AI

Daniel Chalef discusses how LLM synthesis destroys data provenance, making it difficult to trace information back to its sources. He introduces Graphiti, an open-source temporal graph framework, and Zep, an agent memory infrastructure built upon it. This system models provenance as a graph, treating sources as 'episodes' and derived facts as interconnected nodes and edges. This approach enables robust lineage tracing, handles entity merges and fact invalidation through temporal mechanisms, facilitates metadata-driven trust evaluation, and allows for compliant data deletion (e.g., GDPR) based on source support. The benefits include enhanced compliance, improved data veracity, and debuggable AI agents, addressing a critical engineering challenge in LLM applications.

Jul 23, 2026

Why Agentic Systems Need Ontologies — Frank Coyle, UC Berkeley

Frank Coyle discusses the inherent flaws of purely probabilistic LLMs in agentic systems and proposes a neuro-symbolic solution. He advocates for wrapping LLM tool-use loops with external ontologies and validation mechanisms (like RDFS, OWL, and Pydantic) to provide logical guardrails, preventing common, critical errors that probabilistic reasoning alone cannot reliably avoid.

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.