How Unified Context Turns AI Into Real Enterprise Performance - with Ravi Marwaha of Arango

AI agents fail in production not due to model limitations, but because they lack a live, connected understanding of the enterprise environment. Building a real-time context layer—integrating graph, document, and vector data—is essential for agents to reason and act reliably. Explainability remains a non-negotiable engineering requirement; every decision must be traceable to specific evidence to ensure accountability. Organizations should avoid "rip and replace" strategies, instead building context layers on top of existing data infrastructure to ensure agents remain grounded in current business realities. Ravi Marwaha, COO and Chief Technology Product Officer at Arango, illustrates these concepts through applications in customer support, semiconductor engineering, and clinical trial site selection, where context-aware agents significantly reduce resolution times and operational costs by providing the necessary foundation for autonomous, accurate decision-making.