Many organizations struggle to design systems that support true agentic behavior: AI that can plan, reason, and act across multiple steps while continuously self-improving. Traditional RAG pipelines often return irrelevant or shallow results, and fragmented data across vector stores, APIs, and databases creates no unified view of relationships. 

When AI decisions lack explainability, debugging slows and compliance risks increase. To move from prototype to production, AI agents need persistent memory, richer context, and self-learning. Graph intelligence provides these capabilities — with knowledge graphs enhancing RAG and enabling reliable, explainable AI. 

Join us on May 28 to discover how to design architectures that combine LLMs, vector search, and knowledge graphs to support agentic behavior. You’ll see how to orchestrate multi-agent workflows and integrate the tools and data your systems depend on. Register now to walk away with practical patterns for building production-ready systems that help reduce hallucinations and improve relevance.