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.
A practitioner's guide to building a context engine, the reasoning layer that provides AI agents with the necessary organizational context to generate effective and appropriate code. The talk debunks common myths about RAG and large context windows, outlines core requirements for a robust context engine, and shares lessons learned from production.
Martin Keen explains that context, not model intelligence, is the biggest bottleneck in AI. He introduces Context Engineering, its four pillars (Connected Access, Knowledge Layer, Precision Retrieval, Runtime Governance), and advanced techniques like GraphRAG to build more reliable, context-aware AI systems.
Yaser Al, founder of Chatbase, shares his journey of bootstrapping the company from a side project to a $10M ARR business. He details his product-led growth strategy, the evolution of the tech stack from early RAG to a multi-model harness, and his vision for AI agents as 'Chief Customer Officers'.
Richmond Alake from Oracle explains the critical role of Agent Memory in building adaptive AI agents, detailing the four types of memory, the limitations of Retrieval-Augmented Generation (RAG), and the architecture of the modern agent stack.