About the session
Most AI applications today are still built as retrieval systems rather than true reasoning systems. While vector search based RAG pipelines perform well for semantic lookup, they often struggle with contextual continuity, temporal dependencies, multi hop reasoning, evolving enterprise state, and relationship aware intelligence required in real world operational environments.
This hands on session explores how Knowledge Graphs, Context Graphs, and Temporal Graphs can be combined with LLMs to build graph native AI systems capable of maintaining contextual awareness across entities, workflows, events, and evolving organisational knowledge.
We begin by examining the architectural limitations of traditional RAG pipelines in environments involving interconnected enterprise entities, dynamic workflows, and time evolving data. The session then demonstrates practical architectural patterns for integrating:
• Knowledge Graphs for structured intelligence
• Context Graphs for workflow and agent state
• Temporal Graphs for time aware reasoning
• Hybrid graph and vector retrieval
• Graph traversal and multi hop reasoning
• Agentic orchestration and memory systems
Through live walkthroughs and hands on examples, attendees will learn how graph native architectures enable relationship aware retrieval, stateful agent memory, explainable multi hop reasoning, and structured orchestration for enterprise AI agents.
We will also discuss real production challenges involved in scaling these systems, including graph construction, entity resolution, schema evolution, traversal explosion, graph synchronization, retrieval orchestration, and distributed graph performance tradeoffs.
Using examples from healthcare and financial services, attendees will gain practical insights into designing AI systems that move beyond document retrieval toward reasoning-driven intelligence.
This session is intended for AI engineers, architects, and practitioners building production grade GenAI systems where reasoning, memory, and structured intelligence are critical.
