Neo4j

Stop AI Agent Hallucinations: 5 Techniques + Production Patterns - Elizabeth Fuentes, AWS

Jul 11, 2026 · 55:19

Elizabeth Fuentes (AWS) presents five code-based techniques to stop AI agent hallucinations, each with measurable before/after metrics. Semantic Tool Selection filters 29 tools to the 3 most relevant per query, cutting token usage from thousands to under 300 per call. Graph-RAG replaces vector similarity with structured graph queries (using Neo4j), enabling precise aggregation and multi-hop reasoning that vanilla RAG fabricates. Multi-Agent Validation uses an Executor-Validator-Critic swarm to catch fabrications, achieving a 92% detection rate. Neurosymbolic Guardrails enforce business rules in Python hooks that the agent cannot skip, achieving zero rule violations. Agent Steering guides agents to self-correct when soft rules fire, completing tasks without hard failures—demonstrated by booking 50 guests by intelligently splitting into two rooms.

Why your agents need decision traces, not just documents — Zach Blumenfeld, Neo4j

May 29, 2026 · 20:12

Zach Blumenfeld from Neo4j argues that context graphs—a graph-based layer storing decision traces, precedence, and causal chains—extend standard RAG to make AI agents more accurate and explainable. A financial analyst agent, for example, can reject or accept a request by retrieving past decision traces and structurally similar precedents via graph embeddings, not just semantic similarity. Neo4j's one-command tool `uvx create-context-graph` scaffolds a full-stack app with backend, frontend, demo data, and MCP server, supporting 22 built-in domains or custom ontology generation. The underlying `neo4j-agent-memory` package handles entity extraction through a spaCy-to-GLiNER-to-LLM pipeline with deduplication and integrates with pydantic AI, LangGraph, Crew, Google ADK, and others. The episode also covers data connectors for GitHub, Notion, Jira, and Slack, and the ability to store and query decision traces with timestamps, though automated trace quality evaluation is still evolving.

Context Graphs for Explainable, Decision-Aware AI Agents — Andreas Kollegger & Zaid Zaim, Neo4j

May 28, 2026 · 16:39

Andreas Kollegger and Zaid Zaim of Neo4j argue that context graphs transform AI agents from knowledge-driven to decision-aware by providing the "why" behind actions through policies, rules, and precedent. They present a five-stage decision framework: frame the problem with causality and environment, pull global rules and past precedent, run risk value analysis (including reference class validation—e.g., a drug correct 99% of the time may be fatal for the 1%), decide to act or escalate to authority, and write the full reasoning chain into the graph. This last step turns every decision into precedent for future agents. The talk emphasizes explicit risk assessment, such as whether a decision is reversible and what is being maximized, and uses examples like medical prescribing and financial loan eligibility to illustrate how agents must know their reference class before acting.

Connecting the Dots with Context Graphs — Stephen Chin, Neo4j

May 16, 2026 · 17:39

Stephen Chin of Neo4j argues that retrieval alone is insufficient for AI systems, because context graphs—which store relationships, reasoning traces, and decision provenance—enable grounded, auditable answers. He demonstrates with a healthcare example where a generic RAG system returns generic advice while a graph-grounded system knows the patient smokes and has had surgery, tailoring recommendations. The episode walks through Lenny’s Podcast memory demo and a financial services loan-decision app that surfaces prior rejections, margin trades, and fraud risk patterns, making the graph traversal visible. Chin notes Gartner has placed context graphs on the AI hype cycle and Foundation Capital called them a $3 trillion startup opportunity. Neo4j’s open-source agent memory package (short‑term, long‑term, reasoning memory) powers these context graphs, aiming to help engineers escape fragmented enterprise data and build explainable, policy‑aware AI.

Context Engineering: Connecting the Dots with Graphs — Stephen Chin, Neo4j

Nov 24, 2025 · 26:50

Stephen Chin, VP of Developer Relations at Neo4j, argues context engineering powered by knowledge graphs transforms AI from prompt engineering to information architecture, enabling agents with structured memory and retrieval. He demonstrates GraphRAG using Neo4j's knowledge graph builder with supply chain and VEX documents, showing how vector and graph algorithms retrieve specific Jackson library vulnerabilities with CVE details, severity, and remediation versions. Chin contrasts fast two-pass vector+graph retrieval with agentic multi-step traversal via the Neo4j MCP server and Claude Code, which yields deeper results like CVE number, attack type, and upgrade paths. He explains that graphs excel when relationships span two or more facts, using a presentation update example involving himself, colleague Sid, and the GIDS event. Practical applications include role-based access overlays and explainable AI via visualized conversation flows. Resources like free Graph Academy courses, Nodes AI 2026 conference, and graphrag.com support implementation.

Top Ten Challenges to Reach AGI — Stephen Chin, Andreas Kollegger

Jul 22, 2025 · 4:23

Stephen Chin and Andreas Kollegger, curators of the GraphRAG track at AI Engineer World's Fair, use ten sci-fi memes to expose top challenges to AGI—from Memento's short-term memory as prompt engineering to Skynet's unintended consequences, the Matrix's agent simulation, Hal's trust issues, emotions as bug/feature, Frankenstein's creator obligations, Terminator's time travel, Star Wars' language nuance, the Borg's assimilation, and Deep Thought's right questions. They argue graphs and graph technology can solve some of these challenges, inviting the audience to the GraphRAG track to learn which ones. The episode delivers a playful yet pointed framework connecting pop culture to core AGI obstacles.

Practical GraphRAG: Making LLMs smarter with Knowledge Graphs — Michael, Jesus, and Stephen, Neo4j

Jul 22, 2025 · 19:46

Michael Hunger and Stephen Chin of Neo4j present GraphRAG as a method to enhance LLMs by integrating knowledge graphs, achieving more accurate, contextual, and explainable answers than standard RAG. They highlight limitations of vector search, showing GraphRAG improves relevance and reduces hallucinations. They detail a three-step construction process: lexical graphs, entity extraction, graph enrichment with algorithms. They demonstrate open-source tools like Neo4j Knowledge Graph Builder and GraphRAG Python package, and show an agentic approach using domain-specific retrievals. They cite studies showing three times improvement in accuracy.

Agentic GraphRAG: AI’s Logical Edge — Stephen Chin, Neo4j

Jul 21, 2025 · 15:27

Stephen Chin of Neo4j argues that Agentic GraphRAG — combining graph databases with retrieval-augmented generation — overcomes LLM hallucinations and biases by providing structured, relational context. He demonstrates how LLMs fail on reasoning tasks like calculating classroom capacity due to inaccurate anchoring on irrelevant data, and proposes an architecture where vector search first identifies relevant nodes, then graph traversal retrieves related context for the LLM. Chin highlights Neo4j’s MCP server for cypher query generation and memory modules, and cites Klarna’s success: 250K employee questions answered in the first year, 2,000 daily queries, and 85% adoption, replacing their entire SaaS stack. He recommends the Neo4j Certified Developer Program and the Nodes Conference for further learning.

Graph Intelligence: Enhance Reasoning and Retrieval Using Graph Analytics - Alison & Andreas, Neo4j

Jun 27, 2025 · 1:41:16

Alison Cossette and Andreas Kollegger from Neo4j demonstrate how graph data science algorithms enhance GraphRAG systems by connecting, clustering, and curating unstructured data. They walk through running KNN similarity (K=25) on document embeddings, then applying Louvain community detection to identify clusters of near-identical chunks—one community had 49 documents with 0.98 average similarity—which can be collapsed via APOC to improve retrieval efficiency while preserving lineage. Using page rank and betweenness centrality, they show how to surface influential documents and track conversation paths across communities. The session uses a preloaded Neo4j AuraDB database with 17K nodes and 774K relationships from the Agent Neo project, and includes practical cipher queries for projecting graphs and running algorithms. They emphasize using graph analytics to diversify retrieval, manage content hygiene, and build accountable agent systems.

GraphRAG methods to create optimized LLM context windows for Retrieval — Jonathan Larson, Microsoft

Jun 27, 2025 · 15:09

Jonathan Larson of Microsoft Research presents GraphRAG methods, arguing that LLM memory with structure is a key enabler for effective AI applications, and that agents paired with these structures amplify their power. He demonstrates GraphRAG for Code, which uses graph-based understanding to outperform regular RAG on code comprehension, translation from Python to Rust, and even multi-file feature addition like adding jumping to the original DOOM codebase. Larson announces Benchmark QED, an open-source evaluation framework for measuring local and global query quality across comprehensiveness, diversity, empowerment, and relevance. He introduces Lazy GraphRAG, which beats vector RAG on 92% of local and 90% of global queries at one-tenth the cost of million-token context windows, and is being integrated into Azure Local and Microsoft Discovery.

Understanding AI Stakes to Break Production Code: Philip Rathle

Dec 31, 2024 · 23:24

Philip Rathle, CTO of Neo4j, argues that the level of 'stakes' in an AI application determines the production barriers and appropriate solutions, from low-stakes summarization to high-stakes uses requiring knowledge graphs and human-in-the-loop systems. He distinguishes pilot (full autonomy) from co-pilot (human oversight), showing how vector RAG solves moderate stakes but fails for high-stakes needs like tightening bolts on a 737 MAX 9. He promotes Graph RAG for deterministic reasoning and fact retrieval. Attendees share learnings: using LLMs to write code rather than reason directly, adapting to user keyword habits, focusing on few projects with achievable accuracy, building eval pipelines early, and for regulated loans offering three options with explanations instead of a single recommendation.