<p>Existing AI risk assessment frameworks, developed for static predictive models, struggle to address the dynamic, tool-driven failures emerging from autonomous agents and foundation models. Unanticipated harms—ranging from biased decision logic and privacy breaches to adversarial exploits and unsustainable resource consumption—highlight a critical need for a methodology that spans end-to-end, multi-stage pipelines. In response, this article introduces a unified five-domain taxonomy of risks—agentic operation, training data risks, inference behavior risks, output risks, and non-technological factors—each further classified into traditional AI vulnerabilities, those amplified by generative capabilities, and novel hazards unique to generative agents as inspired by IBM AI risk atlas. Through detailed case studies—covering function-calling hallucinations, dynamic bias amplification, privacy leakage across toolchains, and edge-deployment constraints—we examine how specific failure modes arise, interact, and propagate. Building on this analysis, we characterize ten core operational challenges and recommend targeted monitoring and mitigation strategies. Finally, we propose twelve future research and engineering directions—spanning adaptive fairness loops, federated privacy collaboration, schema-guided invocation, policy-as-code governance, and Green AI practices—to guide the design of resilient, transparent, and sustainable agentic systems. Our framework equips researchers, practitioners, and policymakers with a structured foundation for systematic risk assessment, targeted intervention, and continual oversight.</p>