Oct 11, 2026

RAG Needs a Map: Using GraphRAG to Retrieve Connected Context — Nyah Macklin, Neo4j

A hands-on GraphRAG workshop from Neo4j showing how combining vector search with a knowledge graph gives an agent connected context, multi-hop traversal, and explainable answers. It covers the difference between semantic similarity and relationships, LLM-driven entity and relationship extraction under a fixed schema, building an Aura database and Python pipeline, and configuring an Aura agent with similarity search and text-to-Cypher, with a closing check that shows why visible query evidence matters.

Aug 21, 2026

Agentic SDLC at Uber — Uday Kiran Medisetty & Adam Huda, Uber

Uber has transformed its software development with an agentic AI-powered factory, leading to a dramatic increase in engineer productivity. The presentation details six key infrastructure components: a unified model gateway with strict PII and safety guardrails, an MCP gateway for streamlined agent tool access and token optimization, agentified dev pods for rapid execution, a managed skills marketplace, a comprehensive context graph, and the Cortana AI assistant. Adam Huda then demonstrates an end-to-end feature development workflow, highlighting a critical shift to inner-loop validation (stopping short of CI) and automated, managed maintenance loops. The ultimate takeaway is that the bottleneck has moved from technical execution to strategic decision-making: "should we build it?" rather than "can we build it?"

Jul 22, 2026

Your Moat Is Your Data Model — Mike Phipps, Gates Foundation

Mike Phipps from the Gates Foundation details how they built a Strategic Intelligence Platform (SIP) using a Neo4j knowledge graph to serve AI agents. He argues that the true "moat" in an AI-commoditized world lies in an organization's unique data model and tacit knowledge, not in generic AI tools. The platform unifies 25 years of siloed grantmaking data, integrating structured and unstructured information through a rigorous curation pipeline, and is refined via continuous retrieval evaluations to ensure alignment with organizational reporting standards.

Jul 02, 2026

Ex-Google Cloud AI Boss: Your Data Is the Real Moat

Andrew Moore, CEO of Lovelace AI, discusses YottaGraph, a rapidly growing, automatically constructed knowledge graph designed as a context engine for enterprise AI agents. He highlights Lovelace's differentiation from public knowledge graphs by focusing on integrating private enterprise data, the engineering challenges of entity resolution and fast multi-hop reasoning, and the critical importance of graph amendability and auditability for mission-critical applications. Moore also touches upon the future of computer science education, advocating for product management skills and emphasizing the strategic importance of domestically developed open-weights models.

Aug 27, 2025

Distilling 200+ Hours of NeurIPS: What’s Next for AI // Nikolaos Vasiloglou // MLOps Podcast #336

Nikolaos Vasiloglou, VP of Research ML at RelationalAI, shares his extensive analysis of the 2023 NeurIPS conference, distilling over 200 hours of content. Key themes include the dominance and evolution of agentic AI, the state of open-source vs. frontier LLMs, the first signs of deep learning models outperforming XGBoost on tabular data, and the critical rise of verification systems. He also explores the future of AI with data attribution for monetization and the concept of composable, LEGO-like language models.

Aug 22, 2025

Multi Agent AI and Network Knowledge Graphs for Change — Ola Mabadeje, Cisco

A product manager from Cisco's incubation group, Outshift, details a solution that uses a multi-agent AI system combined with a dynamic network knowledge graph to solve critical issues in IT change management. The system integrates with ITSM tools like ServiceNow to automate impact assessment, test plan generation, and pre-production validation in a "digital twin" environment, significantly reducing production failures.