Making your data ready for agentic AI (martinfowler.com)

In a recent exploration of data architecture for autonomous agents, it has become clear that traditional data systems built for human analysts are insufficient for AI-driven technologies. The shift towards agentic AI necessitates a revamped structure to ensure data integrity and usability. Specifically, data must be "agent-ready," with layers that ensure it is trusted, contextual, traceable, governed, and operational. This transformation emphasizes the need for robust data contracts and quality controls, which prevent AI agents from making decisions based on erroneous or outdated data. This shift is significant for the AI/ML community as it highlights the critical importance of data quality in facilitating the effective operation of autonomous agents. Without rigorous data standards and mechanisms that ensure data freshness and accuracy, agents risk acting on flawed information, potentially leading to significant business errors. The proposed solution outlines a medallion architecture with multiple tiers for data handling, ensuring that only validated data reaches the agents. By embedding context directly into the data and implementing strict quality checks, organizations can better equip their AI technologies to perform reliably in real-time environments.