Agentic AI
AI that pursues goals autonomously over multiple steps, such as retrying, routing, or coordinating with other systems, raising new accountability concerns.
Agentic AI refers to systems that can pursue goals autonomously, often across multiple steps and systems. Unlike simple predictive models, agentic systems may decide to retry a failed step, look up additional data, or coordinate with other tools without a human in each loop.
In insurance, agentic AI is emerging in claims triage, customer service bots, and underwriting recommendation engines. The NAIC’s Spring 2026 national meeting flagged agentic AI as a distinct governance risk, citing accountability gaps, error propagation across chained agents, and performance limitations.
Governance for agentic AI must focus on workflow design, not just model accuracy. Regulators want to know who is accountable when an agent makes a chain of decisions, where the human override points are, and how errors are caught. See our guide to agentic AI in claims and glossary entries on Exhibit C and human-in-the-loop.