What are AI agents? And how do they make insurance tools smarter?

It may be the crux of a broker or underwriter's job, but comparing insurance policies is anything but easy. Minor wording differences, structural inconsistencies and evolving definitions can all shift how that cover is interpreted.

This process is rife with risks for human error. So we turned to tech. Traditional tools, most of what is on the market today, compare policies by:

• Scanning documents for identical or different strings of text

• Highlighting changes

• Offering side-by-side viewing

• Maybe keyword search or tagging.

Utilising these tools has reduced manual reading for the industry, but then it's back to the user to make sense of what has been highlighted. The tools ultimately have no concept of meaning, intent, or context. They can't tell if changes were significant or legally consequential. And they don't understand insurance language. They just compare the words.

But now we have AI. AI introduces a layer of semantic understanding. Through it, we can:

• Recognise that two phrases mean the same thing, even if worded differently

• Pick up on nuanced shifts in meaning, not just in text

• Spot structural similarities or inconsistencies, even if layout or wording changes

• Interpret content like a person would, not just as data

• Cluster or classify clauses by what they're doing, not just what they say.

Agentic AI: A system of minds

Most AI tools use a single model trained to handle everything. Operating like this is akin to hiring one very fast and very clever person and asking them to read two documents, compare every clause, understand the context, check the definitions, decide what matters, and hand you a final answer in one step.

That sounds efficient. But in reality, too much gets bundled together. As intelligent as AI may be, a single model might gloss over a change in intent, flag something irrelevant, or miss a connection between clauses. It's working quickly, but not necessarily clearly.

Agentic AI breaks that process into pieces.

Agentic simply means that the AI is made up of agents: small, specialised models, each trained to do one specific task. One might extract defined terms. Another compares exclusions. A third tracks how a clause has changed over time. They don't all run at once, and no individual agent tries to solve the whole thing. Instead, they pass their output along, each building on the last.

That separation makes the system easier to control and easier to trust. You're not relying on one giant model to make sense of everything. You're getting a sequence of focused decisions and a clearer view of what's changed, what hasn't, and what needs attention.

This can be hard to visualise. We recommend checking out this video on AI agents. We have already skipped ahead to the useful part.

Built for layered logic

Insurance policies are doubly complex. They're long and they're layered. A change in one section can affect the meaning of another. A new definition may shift how terms are used throughout the document. An exclusion added in one area can alter the scope of cover elsewhere. References link clauses across pages, creating dependencies that are hard to pick up at first glance.

Agentic AI is built to follow this kind of structure. With each "agent" processing a specific task – extracting clauses, tracking definitions, mapping references – the system moves through a policy the way a person would: step by step, with context carried forward.

Rather than treating each section in isolation, the agents maintain connections between terms, clauses and exclusions. That layered processing supports a more accurate view of the document. It looks at both how it is written and how it fits together.

Clarity in Every Drop

Built on this agentic architecture, Raindrop processes policies using a system of AI agents trained on real insurance documents. Each agent handles a specific task, from identifying defined terms to mapping exclusions, producing a structured comparison that reflects how professionals actually interpret cover.

The result is a more accurate, context-aware view of the document, with outputs you can trace, trust and act on.

Book a demo today.