Observability, benchmarking, evaluation, and metrics serve distinct purposes in understanding AI agent behavior and performance, often leading to confusion when teams conflate them. Observability involves continuous data collection to reveal agent behaviors and decision-making processes, while benchmarking is episodic, comparing performance against standards. Metrics provide quantitative measures but lack the context necessary for true observability, which is essential for understanding agent decisions and anticipating failures. Evaluation requires predetermined criteria to assess whether agents meet specific objectives. In practice, observability is crucial for analyzing AI agent failures, followed by metrics to quantify impact, and evaluation to gauge severity. Custom metrics are necessary when standard benchmarks don't capture unique behaviors, and excessive data collection can hinder rather than help, especially when it overwhelms the ability to derive actionable insights. The relationship between these concepts varies across domains, with AI agents requiring observability to track decision reasoning and benchmarks to test robustness, unlike traditional software systems. The distinction between monitoring and observability stems from the latter's need to understand complex decision processes, and metrics become benchmarks when used for comparative assessments. Ultimately, observability provides ongoing operational insights, while evaluation supports structured decision-making, with metrics informing benchmarks to ensure context-specific performance assessments.