AI agents are rapidly being adopted by enterprises, with predictions that one-third of software applications will include agentic AI by 2028. These agents operate autonomously, creating a rich layer of metadata that captures their interactions and decision-making processes. This metadata, which includes user prompts, tool calls, and decision confidence scores, is essential for understanding how AI agents arrive at their conclusions. Chris Glaze from Snorkel AI highlights that this metadata provides insights into the agents' reasoning, making it valuable for retraining and compliance. However, the collection and application of this metadata remain fragmented, as noted by Greg Jennings from Anaconda. There are two main types of metadata: the foundational knowledge that allows AI agents to function and the operational data that agents generate themselves. This operational data includes operational metrics like latency and memory use, as well as reasoning and interaction details. Experts emphasize that the most useful data comes from tracing the agents' decision-making paths, often stored as JSON objects, which can facilitate debugging and continuous improvement. Agentic metadata has multiple practical applications. It aids in debugging by identifying the root causes of failures, as seen with an internal SQL-generating agent at Incorta. Continuous feedback loops for retraining can help agents adapt to new user needs and avoid past mistakes. Furthermore, this metadata can lead to significant cost optimization by highlighting inefficiencies in AI workflows, such as unnecessary API calls. In governance and compliance, agentic metadata serves as a tool for auditing and security, ensuring that AI systems operate transparently and effectively.