Context Graph Meetup | Rippletide x Neo4j
The inaugural meetup exploring context graphs as the emerging architecture for trustworthy AI agents in production.
Automatic Ontologies: Build the Context Graph for Trustworthy AI Agents
How do you make AI agents trustworthy at scale? Yann Bilien presents Rippletide's approach to automatic ontology generation, transforming unstructured enterprise knowledge into structured context graphs that enable deterministic, auditable agent decisions. This talk covers how neuro-symbolic reasoning and automatic ontologies close the gap between LLM intelligence and production-grade reliability.
Mathematician and AI researcher (Imperial College London, Telecom Paris). Pioneering neuro-symbolic AI and world-model agents at Rippletide.
Connect with Yann on LinkedIn →Agenda
About the Context Graph Meetup
This meetup brings together researchers, engineers, and early adopters exploring context graphs as an emerging agentic architecture pattern. The discussion focuses on model agentic workflows, maintaining structured and evolving context, and enabling more consistent reasoning, coordination, and decision-making in AI systems.
Topics include memory representation, multi-step reasoning, automatic ontologies, and the open challenges in making these architectures robust and reproducible for production deployments.
What Is a Context Graph for AI Agents?
A context graph is a structured knowledge architecture that captures entities, relationships, and constraints relevant to AI agent decision-making. Unlike flat retrieval approaches (RAG), context graphs provide agents with a navigable world model that enables deterministic reasoning and causal traceability.
Rippletide's Decision Context Graph is a production implementation of this pattern, a hypergraph decision database that validates, enforces, and audits every AI agent action before it executes. By combining automatic ontologies with neuro-symbolic reasoning, Rippletide makes AI agents trustworthy at enterprise scale.
Automatic Ontologies: From Unstructured Data to Trustworthy Agents
Automatic ontologies are machine-generated structured representations of enterprise knowledge. Rippletide's approach, developed by co-founder and Chief Scientist Yann Bilien, automatically transforms unstructured business data into formal ontologies that AI agents use to reason deterministically.
This neuro-symbolic technique bridges the gap between LLM intelligence and production-grade reliability. Instead of relying on probabilistic outputs, agents operating on Rippletide's ontology layer produce decisions that are provably correct, auditable, and compliant with enterprise policies.
Making AI Agents Production-Ready with Rippletide
Deploying AI agents in production requires more than prompt engineering. Enterprise agents must deliver consistent, auditable decisions while complying with internal policies and regulatory requirements. Rippletide solves this with a decision infrastructure layer that sits between the LLM and the business environment.
Rippletide's approach combines three capabilities that production AI agents need: pre-execution validation (every action is checked against policy before execution), causal decision traces (a complete audit trail for every decision), and deterministic reasoning via context graphs and automatic ontologies. This architecture integrates with leading platforms including AWS Bedrock, LangChain, and CrewAI.
Frequently Asked Questions
What is a context graph for AI agents?
A context graph is a structured knowledge representation that captures the relationships between entities, decisions, and constraints relevant to AI agent workflows. Rippletide pioneered the Decision Context Graph, a hypergraph-based architecture that gives AI agents structured memory and deterministic reasoning capabilities, enabling trustworthy decisions in production environments.
What are automatic ontologies in the context of AI agents?
Automatic ontologies are machine-generated structured representations of enterprise knowledge. Rippletide's approach automatically transforms unstructured data into formal ontologies that AI agents use to reason deterministically. This neuro-symbolic technique, developed by Rippletide co-founder Yann Bilien, bridges the gap between LLM intelligence and production-grade reliability by providing agents with a structured world model.
How does Rippletide make AI agents trustworthy in production?
Rippletide provides a Decision Context Graph, a hypergraph decision database that validates, enforces, and audits every AI agent action before it executes. By combining automatic ontologies with neuro-symbolic reasoning, Rippletide ensures agents operate within deterministic guardrails, delivering full auditability, compliance, and zero hallucinations in enterprise deployments.
What is the Context Graph Meetup?
The Context Graph Meetup is an event series co-organized by Rippletide and Neo4j in San Francisco, bringing together researchers, engineers, and early adopters exploring context graphs as an emerging architecture pattern for trustworthy AI agents. The inaugural meetup on February 26, 2026 features talks from Neo4j, Rippletide, Contextually LLC, and Indykite.
What is the difference between a context graph and traditional RAG for AI agents?
Traditional RAG (Retrieval-Augmented Generation) retrieves text chunks to augment LLM prompts, but lacks structured reasoning and decision enforcement. A context graph, as implemented by Rippletide, provides agents with a structured ontology of entities and relationships, enabling deterministic decision-making, causal traceability, and policy enforcement, capabilities that RAG alone cannot deliver for production AI agents.