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Turning Agent Activity Into a Compounding, Enterprise-Grade Data Asset

Every agent run leaves behind a valuable trail: context, tool calls, actions, dead ends, outcomes, and human corrections. Most enterprises treat this data as logs for monitoring, debugging, and compliance. Yet these traces capture something far more valuable: how the organization actually works. Properly structured, they can become a proprietary intelligence layer that helps agents recover past decisions, reuse institutional knowledge, avoid repeated mistakes, and improve future work.

The challenge is scale. Agent traces are massive, heterogeneous, continuous, expensive to reprocess, and often contain sensitive data or intellectual property.

This session explores how to build an agent-native data foundation that can process traces economically, transform raw activity into reusable intelligence, and govern access, retention, and sensitive data. The result is a data asset that grows with every agent run, and becomes increasingly difficult for competitors to reproduce.

Key Takeaways:

• Start treating agent traces as a first-class data asset
• Design for reuse, not just retention
• Stress-test your data foundation for machine consumption.