What Context Really Means in Data Engineering and AI

Context engineering has become a critical discipline for ensuring trustworthy AI outcomes as agents replace human analysts as the primary consumers of organizational data. Defining context requires integrating formal structures like semantic layers and data catalogs with informal tribal knowledge, all while maintaining high-fidelity, benchmarkable artifacts. Soham Azumdar, CEO of Wisdom.ai, emphasizes that modern data engineering must shift toward a probabilistic mindset, where systems are continuously optimized by monitoring agent traces and identifying gaps in understanding. Because context is inherently fragmented across silos, successful implementations rely on a composite of data structures—including relationship graphs and trajectory logs—to guide agents effectively. Ultimately, the most significant challenge in the field remains the lack of rigorous measurement for context quality, which is essential for preventing drift and ensuring that agentic systems operate with precision and cost-efficiency.