To effectively manage AI agents, organizations must prioritize observability that extends beyond model calls and includes the complete reasoning chain, code paths, and tools activated. Shahar Azulay, CEO of Groundcover, emphasizes the importance of real-time performance metrics—like token usage, latency, and throughput—alongside traditional telemetry to preemptively identify issues such as hallucinations and risky actions. As AI agents increasingly execute code and access sensitive data, security-focused observability is crucial. This means inspecting payloads, validating integrations, and ensuring every agent action is authorized.
Graham Neray, co-founder of Oso, highlights that observability should encompass tool use and data source interactions. By categorizing the risk levels of agent actions and monitoring for anomalies, organizations can enhance their risk management strategies, ultimately boosting business value and ROI as they integrate AI agents into operational workflows.
Source link
