Core Insights - The foundational truth of AI is that it relies heavily on data, and without data, AI agents cannot function effectively [1][2] Data Accessibility and Management - Google emphasizes that enterprise teams face challenges in building agentic AI due to the scattered nature of data across various platforms, necessitating access to accurate and well-documented data along with its metadata [2][3] - Google has partnered with Ab Initio to create a "neutral hub" that connects multiple data sources and standardizes metadata, enhancing data governance and auditability [3] Evaluation and Performance - AWS highlights that the complexity of AI agents lies not just in generating responses but in ensuring reliability through comprehensive evaluation of the entire system's performance [4][8] - Effective evaluation requires detailed trace files and "golden" datasets for regression testing, allowing teams to monitor agent performance and identify failures [8][9] Data Preparation Challenges - Many enterprise AI projects struggle with the "last-mile" data problem, where operational data is messy and needs to be processed quickly for real-time decisions [11] - Empromptu's "golden pipelines" automate the data preparation process, ensuring that data is cleaned and structured while maintaining governance controls [12][13]