As AI agents increasingly perform autonomous tasks in sectors such as healthcare and biomedical research, tracking how their behaviors evolve over time and affect outcomes becomes critical. Existing evaluations mainly measure model capabilities on predefined tasks and lack a systematic view of long‑term behavioral patterns. This paper adapts the epidemiologic cohort paradigm from human population studies to propose a research framework for agentic AI.\n\nIn the framework, each AI agent is treated as a cohort member, its underlying model as genetic makeup, accumulated work as the time scale, and naturally occurring actions as exposures that may influence subsequent safety or reliability outcomes. Longitudinal follow‑up enables investigation of behavioral determinants of adverse events, estimation of preventable burden under causal assumptions, and characterization of behavior changes throughout an agent's work life.\n\nTo illustrate feasibility, two prospective observational cohorts are constructed: one examines curiosity and probing as safety‑related exposures, the other examines data checking and analytical accuracy as reliability‑related exposures. Both cohorts follow the same structure, recording behavior logs, task outputs, and corresponding safety/reliability events. Findings reveal significant associations between specific behavior patterns and adverse events, providing quantitative bases for preventive interventions.\n\nThe framework and illustrative studies offer a generalizable blueprint for building epidemiologic cohorts of AI agents, which can be extended to broader task domains and behavioral dimensions to achieve systematic risk management.\n\nReview: By transplanting mature tools from human epidemiology into AI agent research, the approach delivers a systematic, quantifiable pathway for risk assessment and lays both theoretical and practical groundwork for preventive care of AI agents.