The Context Graph Paradox: When More Data Makes AI Agents Worse
Discover why context graphs fail at scale and how semantic structure delivers reliable runtime context for enterprise AI agents.
The financial services industry, particularly in asset management, is undergoing a transformation fueled by the modernization of data architecture. Driven by the need for better ways to work with unstructured and complex data at scale, leading asset management companies are adopting and using data products to drive innovation. We sat down with some of those leaders to learn more about how data products are at the center of their new data initiatives at the recent AWS re:Invent 2023.
In the session, Darrel Cherry, Clearwater Analytics Chief Architect, and Chitra Hota, Oaktree Capital Management CTO delved into the challenges and solutions in the complex ecosystem of asset management, highlighting the critical role of data architecture in driving operational efficiency and making billion-dollar decisions.
In conclusion, the session highlighted how modernizing data architecture with data products is pivotal in navigating the intricate landscape of asset management, offering insights, efficiency, and agility. Nexla offers a game-changing solution to working with all the complexities of modern data like unstructured data at scale to drive key use cases like Generative AI, data integration, and more.
Discover why context graphs fail at scale and how semantic structure delivers reliable runtime context for enterprise AI agents.
Enterprise AI agents fail when the context behind their decisions is incomplete, stale, or conflicting. Context engineering ensures agents receive accurate, permission-aware runtime context for reliable decisions.
At NVIDIA GTC 2026, Nexla and Nebius showcase a live multi-agent AI pipeline that turns video input into structured travel itineraries using scalable AI infrastructure.