From Context Windows to Context Graphs: The Next Generation of AI Systems

Abstract Large Language Models (LLMs) have captured everyone’s imagination—but under the hood, they’re limited by a simple fact: they can only “remember” what fits into a context window. That’s why they sometimes forget, contradict themselves, or fail to connect the dots across conversations. In enterprise settings, these failures aren’t just quirks—they’re risks to adoption, reliability, and ROI. This talk explores the evolution of AI memory and reasoning: • RAG (Retrieval-Augmented Generation): fetching external knowledge when the model doesn’t know enough. • CAG (Context-Augmented Generation): enriching answers with curated, structured context for higher accuracy. • Context Graphs: a breakthrough that gives AI something closer to long-term memory, explainability, and reasoning. We’ll also show how agentic AI design patterns—like Planner-Executor, Critic-Actor, and Memory-Augmented Agents—become powerful when combined with these advances.

Why this talk matters (Strategic ROI) • Reduce wasted cycles: Minimize hallucinations and rework, lowering the cost of errors. • Increase productivity: Agents with reliable memory scale beyond demos to handle real workflows. • Build trust & adoption: Explainable AI reduces resistance from compliance, risk, and business stakeholders. • Future-proof investment: Context graphs evolve with data, ensuring long-term adaptability.

Who should attend • AI newcomers curious why models forget and how memory can be extended. • Engineers seeking practical design patterns to build production-ready AI. • Leaders looking to understand how better AI memory drives ROI and enterprise readiness.

Key takeaway AI is moving beyond prompts and short-term context. By the end of this session, you’ll see how the field is evolving—from context windows → RAG → CAG → context graphs—and why this evolution is not just a technical leap, but a strategic one, enabling reliable, scalable, and ROI-driven Generative + Agentic AI systems.