This article argues that data teams should transition to context engineering, integrating data governance, engineering, and science to create reliable knowledge sources for AI agents. It highlights the need for a structured context stack to ensure accurate answers and effective performance from these agents.

This article outlines how Agent Bricks creates tailored AI agents using your organization’s data. It emphasizes automated evaluation, continuous improvement through human feedback, and offers resources for getting started with AI agents effectively.

The article argues that while traditional systems of record aren't dying, they are evolving in response to automation and AI agents. A reliable source of truth is still essential for enterprises, but the way that truth is accessed and managed is changing. The author emphasizes the need for clear definitions and governance as workflows become more complex.

Systems of record may be perceived as becoming obsolete due to the rise of AI agents that automate tasks and generate data. However, the author argues that these systems will become increasingly essential in governing AI activities, managing data access, and ensuring compliance. The future may see a transformation of systems of record into control layers, focusing on agent governance rather than merely being places where work occurs.