Automation, AI, and Agentic Systems: Three Technologies Every Claims Leader Needs to Understand

Automation. AI. Agentic systems. These words dominate insurance conferences and boardroom conversations, but most people lump them together as if they’re stages in a single evolution. They’re not.

As Kyber’s CEO, Arvind Sontha, often explains, these are three distinct technologies with different strengths. Automation provides tight control, AI delivers flexibility, and agentic systems aim for self-direction but remain too unreliable for compliance-critical work. For claims leaders, understanding the differences isn’t just technical trivia. It’s the foundation for making the right bets as you modernize your workflows.

Automation: Controlled and Predictable, But Brittle at the Edges

Automation is the oldest of the three, and for good reason. It thrives when consistency is the top priority. Think of it as strict “if X then Y” logic — a letter goes out every 30 days, an acknowledgement is triggered the moment a claim is logged.

That reliability is its strength. As Arvind puts it, automation is what you need “for certain pieces of the process, like status letters that have to go out every 30 days.” It ensures compliance where the rules are clear and repeatable.

But the same rigidity that makes automation dependable also makes it fragile.

“You’d try to pipe in all sorts of delay reasons and the next thing you know, a new regulation pops up, the whole system breaks because it can’t handle it.”

The moment the environment changes, whether a new jurisdictional requirement or a novel exception, the rules fall apart.

In claims, where exceptions are constant, this brittleness is a real limitation. Automation works best when the task is straightforward and variance is the enemy. It falters when nuance, interpretation, or judgment is required.

AI: Flexible and Outcome-Driven

Where automation breaks down, AI offers a different path. Instead of hardcoding every rule, AI starts with the desired outcome and works backward.

“AI says, I know the outcome I’m looking for. Let me take the data I have and synthesize it.”

That shift is powerful in claims, where the inputs are messy: adjuster notes in free text, policy language that varies by jurisdiction, regulatory rules that change state to state. Instead of brittle logic, AI can pull all of this into a coherent, compliant letter.

Take status updates. With traditional automation, you’d need to program every possible delay reason into the system. One new exception, and the rules collapse. With AI, the system can understand the intent, keep the policyholder informed with the right context, and generate a letter that meets the standard without needing a new rule every time.

The benefit for claims teams is resilience. AI doesn’t eliminate oversight, but it reduces the burden of constant template rewrites and rule maintenance. That means faster turnaround, fewer errors, and more time for adjusters to focus on complex claim decisions instead of paperwork.

Agentic Systems: Ambitious, Workforce-Like, and Not Ready for Compliance

If automation is about rigid rules and AI is about flexible outcomes, agentic systems aim for something different: self-direction. Think of it less like a tool and more like a workforce. An agentic system doesn’t just complete tasks, it reasons about what tasks to do in the first place, chooses the right tools, and executes.

“Agentic isn’t just chaining together LLM calls. It’s a core model reasoning through a task, deciding which steps are required, and choosing when and how to execute them.”

That’s a radical shift. Instead of programming workflows, you’re asking the system to design the workflow itself. In insurance terms, that could mean an agentic system looking at an open claim and deciding:

• What letter should be sent?
• What channel should we send via?
• What compliance notes are needed?

It’s the dream of a digital workforce that manages itself.

But the challenge is reliability. A single model call might be 70 percent accurate. String a series of them together and failure multiplies. “If I give you a model and say it has a 70 percent success rate, and now you chain five calls together, you multiply failure. The whole thing breaks down.”

This compounding error problem makes agentic risky in claims. One missed step isn’t a small mistake. Rather, it can mean a regulatory violation or a lawsuit.

Still, the analogy to a workforce is instructive. Just as you would measure, monitor, and govern a team, agentic systems will eventually need similar oversight frameworks. They might prove powerful in areas with lower stakes or where experimentation is safe. But in claims correspondence, where accuracy and auditability are mandatory, agentic remains more aspiration than reality.

So What? Rethinking the Toolbox

The easy mistake is to see automation, AI, and agentic systems as a single staircase. Automation was yesterday, AI is today, and agentic will be tomorrow. But the reality is not sequential. These are three different technologies with different strengths.

For claims leaders, that means the question isn’t “what’s next” but “what’s right.” Automation still has a role where repeatability and control matter most. AI is already delivering results in the messy middle, where outcomes need to be clear but the paths to get there are variable. And agentic, while promising, remains too unreliable for compliance-critical work.

The mindset shift is simple but powerful: stop treating technology as a maturity ladder and start treating it as a toolbox. The winners in this next phase of insurance will be the leaders who know which tool to pick, and when.

How Kyber Fits

This is exactly how we’ve built Kyber. We don’t fit neatly into the industry’s shorthand of “AI” because we’re not just one thing. We combine automation and AI to give claims teams both speed and compliance.

On one side, Kyber uses automation to enforce the guardrails: when letters are triggered, how data is mapped, and where audit trails are captured. On the other, Kyber uses AI to flexibly draft, edit, and personalize correspondence without brittle rule sets. Together, it means adjusters get drafts instantly, leaders get compliance by default, and teams scale without adding overhead   .

That’s why when people call us “an AI solution,” we usually say: we are, kind of. But we’re also automation. And above all, we’re a better tool for humans. Kyber is built so adjusters, managers, and compliance teams can focus on claims, not paperwork — with the confidence that every letter is fast, accurate, and audit-ready.