Validating Agentic AI Systems Requires Beyond Component Testing

Agentic AI systems, which involve complex multi-step behaviors like planning, tool use, memory, and adaptation, present significant challenges for traditional validation methods. Unlike simpler AI models, their acceptable behavior depends on how decisions unfold over time and under varying environmental conditions, moving beyond simple input-output evaluations. A comprehensive survey of 257 papers across various fields, including agent evaluation, software assurance, and regulatory guidance, identifies five key dimensions for validating agentic systems: behavioral, safety, temporal, regulatory, and multi-agent concerns. While behavioral evaluation is relatively mature, areas like temporal validity, runtime evidence maintenance, regulatory legibility, and assurance for open-ended multi-agent systems are still underdeveloped. The paper highlights these gaps through case studies in safety-critical domains such as medical care, industrial operations, and smart mobility. It concludes by proposing a research agenda focused on bounded-autonomy specifications, adversarial trajectory generation, runtime monitoring, and audit-ready evidence structures, asserting that trustworthy deployment hinges on validating entire trajectories within their context.