What is Agentic AI for Insurance?

What is Agentic AI?

Agentic AI is a form of AI system capable of acting autonomously, coming up with solutions and taking actions without direct human oversight. Agentic AI systems are able to handle complex assignments, often involving multiple steps, tasks, or systems. 

To accomplish its objectives, agentic AI commonly uses a framework with three primary steps: perceive, reason, and act.

• Perceive: The agent can gather and process a large amount of data from many different sources, allowing it to assemble a picture of the situation.
• Reason: An LLM analyzes the data from the previous step to understand the problem, and generates a plan to address it and achieve the objective.
• Act: Using connections to other systems (such as APIs), the agent can take actions to execute on its plan.

For example, a shipping company might set up an agentic AI system to optimize package delivery. The system would then use the above framework to achieve that goal:

• Perceive: The agentic AI system would ingest data like real-time traffic and road status updates, package priority, and truck location.
• Reason: The AI would continuously analyze the data as it was received,  determine the most efficient delivery routes to get the right packages to the right place at the right time, and update the route as needed to respond to changing conditions.
• Act: The AI would then push the route guidance to drivers, helping them get where they need to be faster.

An ongoing data feedback loop allows agentic AI to learn from results, and continuously improve its effectiveness over time. For businesses, this means a tool that grows more valuable as it’s used over time.

Agentic AI vs AI Agent

Confusingly, “agentic AI” and “AI agent” are not the same. Agentic AI refers to the system as a whole, and the idea of the AI solving complex tasks. AI agents are the actual AI tools that execute tasks. An agentic AI system might have several individual AI agents as part of its workflow, each with its assigned task.

Going back to our delivery example, the agentic AI system for package delivery optimization might include an agent that ingests real-time traffic data from mapping apps, an agent that monitors updates on road closures or other issues, an agent that checks package priorities against current location, and so on. All of these agents working together on their individual tasks contribute to the larger agentic AI system addressing the main objective of efficient package delivery. 

Agentic AI vs Generative AI

Agentic AI and generative AI might seem similar at first glance, and in fact both are underpinned by LLM technology. However, they’re actually two distinct AI types with different capabilities and different use cases.

Generative AI (gen AI) handles discrete tasks, producing output in response to a user prompt. If the user wants to refine the results or generate new content, they need to submit another prompt. Gen AI is most often used to create visual or written content, or to analyze data by finding patterns within it. While gen AI might make personalized recommendations to the user based on the prompt, it does not actually make decisions.

Agentic AI does make decisions. An agentic AI system will be given a predefined objective, then use the perceive-reason-act framework to determine the best way to achieve that objective. This means agentic AI is ultimately much more capable than gen AI: while an agentic AI workflow might include gen AI, it can accomplish much more complex tasks, and do more to actually solve the problem. 

For example, say a user requests a recommendation for a certain kind of product. A gen AI might respond by pulling up a list of products from across the web that meet the user’s criteria.  An agentic AI, on the other hand, could take the user’s criteria, identify the product that best fits their stated needs, compare the item’s price across different sellers, and purchase the best-priced option available from a seller with a minimum number of positive reviews.

Agentic AI in Insurance

Like many industries, insurance stands to significantly benefit from agentic AI capabilities. Agentic AI offers great potential for automating repetitive or low-value tasks, freeing up human underwriters and other insurance professionals to focus on higher-value work that requires their expertise and judgement. 

Sample Use Cases for Agentic AI in Insurance

One of the most exciting aspects of agentic AI is its adaptability - with the right implementation, its potential uses are limited only by insurers’ imaginations and technical resources. Here are just a few examples of complex, repetitive tasks where insurers might leverage agentic AI:

• Marking high-risk renewals for review. An agentic AI system could monitor all upcoming policy renewals, and proactively flag any policies with attributes that fall outside certain parameters to ensure they receive an underwriter’s attention prior to renewal.
• Pulling all recent news about a potential insured organization. When a submission comes through the system, AI can save the underwriter time by proactively searching the web for any recent news about the potential insured, and summarizing that information for underwriters.
• Identifying similar policies and surface past winning strategies. For high-value submissions, AI could analyze all past underwriting activity for similar policies, and make recommendations for underwriters based on what has previously proven successful.
• Drafting decline emails based on submission data. Based on appetite and other qualifying data (such as business type or state), an agentic AI system might identify submissions to decline, and draft an email explaining the declination for the underwriter to review and approve.

While these examples offer a few illustrations of how insurers might benefit from agentic AI, they only scratch the surface of what agentic AI can offer. Agentic AI represents a huge opportunity for insurers to increase productivity and output through sophisticated, autonomous problem solving, with continual improvement over time. For underwriters, support from agentic AI means they can focus more on their most valuable work, and ultimately write more (and more profitable) business.

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