Stephen Chin of Neo4j argues that traditional RAG is insufficient because AI agents lose the reasoning behind past decisions. He introduces Context Graphs as a solution to capture the 'why' behind decisions, creating a queryable system of precedent that provides grounded, explainable, and auditable results.
Leonie Monigatti from Elastic provides a practical guide to agentic search, arguing that effective context engineering is not just a retrieval problem, but a search problem. The workshop explores the trade-offs between specialized tools (like semantic search) and general-purpose tools (like shell and SQL execution), offering a "low floor, high ceiling" framework for building a robust and efficient retrieval stack for AI agents.
Professor Kyunghyun Cho, a co-author of the first paper on attention, discusses the future of AI. He argues that today’s models have already captured most correlations in passive data, making the real challenge about actively choosing which data to collect. He also explores the open debate around world models, the surprising lack of coding agent adoption among his students, and the foundational work that led to Retrieval-Augmented Generation (RAG).
Cedric Clyburn explains the Models-as-a-Service (MaaS) pattern, detailing how organizations can build their own private AI infrastructure to deploy models like LLMs securely and at scale. He covers the benefits over public APIs, including cost control, data sovereignty, and lifecycle management, and outlines a technical architecture using Kubernetes, API gateways, and observability tools.
Simon Hørup Eskildsen, founder of turbopuffer, shares his journey from scaling Shopify's infrastructure to creating a new search engine for the AI era. He discusses how a prohibitively expensive experiment at Readwise inspired him to build a cost-effective vector search solution based on object storage and NVMe. Eskildsen breaks down turbopuffer's architecture, its role in cutting costs for companies like Cursor and Notion, his philosophy on building a 'P99' engineering team, and how agentic workloads are changing the future of retrieval.
Notion's Co-Founder, Simon Last, discusses their evolution from a writing assistant to a platform for custom AI agents. He covers the technical hurdles of semantic indexing, the internal shift toward using coding agents to build Notion, and the fundamental transition from a tool where humans do the work to one where humans manage a swarm of agents.