In the evolving agentic era, traditional observability methods fall short as AI systems transition from experimental stages to production, often breaking in subtle and unpredictable ways when prompts, retrieval, tools, and memory intersect. Atin Sanyal, co-founder and CTO of Galileo, presents a modern evaluation framework designed for agent-based systems, emphasizing a practical and metric-driven approach to identify and rectify failure modes early by thoroughly instrumenting the agent loop in areas like tool quality, error rates, latencies, and business KPIs. He illustrates this through a real-world example involving a stock-trading workflow, highlighting how issues such as brittle retrieval and flawed logic lead to drift, and demonstrating how enhanced telemetry can enable swift and targeted solutions. An upcoming webinar will further explore an agent observability and evaluation playbook aimed at creating reliable AI systems, offering methods to trace root causes and promote continuous improvement through hard metrics, while also discussing how to integrate agent observability with minimal effort.