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🤖 '''Agentic AI''' describes artificial intelligence systems capable of autonomously pursuing complex, multi-step goals with minimal human intervention — planning actions, executing them, evaluating results, and adapting their approach in real time. In the insurance context, agentic AI represents a significant evolution beyond the [[Definition:Machine learning|machine learning]] models traditionally used for discrete tasks like [[Definition:Risk scoring|risk scoring]] or [[Definition:Fraud detection|fraud flagging]]. Instead, an agentic AI system might independently manage an entire [[Definition:Claims management|claims]] workflow: receiving a first notice of loss, gathering documentation, querying external data sources, assessing coverage under the relevant [[Definition:Insurance policy|policy]], calculating the [[Definition:Reserve|reserve]], and communicating a settlement offer to the [[Definition:Policyholder|policyholder]] — all without a human adjuster touching the file until an exception arises.

⚙️ These systems typically operate by combining [[Definition:Large language model|large language models]], specialized domain tools, and orchestration logic that allows the AI to break a high-level objective into subtasks, invoke appropriate resources for each, and iterate based on outcomes. An [[Definition:Insurtech|insurtech]] deploying agentic AI for [[Definition:Underwriting|underwriting]], for example, might build a system that autonomously identifies submission data gaps, emails brokers for missing information, retrieves third-party exposure data, runs pricing models, and drafts a quote — escalating to a human underwriter only when the risk falls outside pre-defined authority parameters. The technology is still maturing, and most insurance implementations currently operate under tightly bounded autonomy with human-in-the-loop checkpoints. Regulatory expectations reinforce this caution: frameworks from the [[Definition:National Association of Insurance Commissioners|NAIC]], [[Definition:European Insurance and Occupational Pensions Authority|EIOPA]], and the [[Definition:Monetary Authority of Singapore|Monetary Authority of Singapore]] all emphasize that insurers must maintain meaningful human oversight over consequential decisions, regardless of the sophistication of the underlying AI.

🚀 The potential impact of agentic AI on insurance operations is profound. By automating not just individual decisions but entire processes end-to-end, these systems promise dramatic improvements in speed, consistency, and cost efficiency — particularly for high-volume, lower-complexity lines such as personal auto, travel, or small commercial insurance. Yet the risks are equally significant: an autonomous system that misinterprets policy language, hallucinates facts, or applies flawed logic across thousands of claims before anyone notices could generate substantial financial and [[Definition:Reputational risk|reputational]] exposure. This tension between transformative efficiency and amplified risk makes [[Definition:AI governance|AI governance]] and [[Definition:AI ethics|AI ethics]] frameworks essential companions to agentic AI deployment. Insurers that develop robust guardrails, monitoring systems, and escalation protocols will be best positioned to harness agentic AI's capabilities while managing its novel failure modes.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Artificial intelligence]]
* [[Definition:Large language model]]
* [[Definition:AI governance]]
* [[Definition:Straight-through processing]]
* [[Definition:Claims automation]]
* [[Definition:Robotic process automation]]
{{Div col end}}

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PlumBot: Bot: Creating definition