Don't Replace RPA With AI Agents—Layer Them Under Your Bots

You're a claims manager at a mid-sized insurance company. Your team processes hundreds of auto claims a day, and you've been told to 'get ready for AI agents.' But when you look at the tools, you're not sure if you should rip out your RPA bots or keep them. Here's the answer: keep the bots, and put the agents on top. That's the dominant emerging architecture, and it's the only one that makes financial sense.

The RPA-Agent Split: Who Does What

RPA is deterministic. It follows predefined, rule-based scripts to do repetitive tasks like copying data between applications and filling forms (Robotic process automation – Wikipedia). It's brittle, but it's fast, reliable, and cheap. AI agents, on the other hand, use large language models to reason, plan, and call tools—they handle variability but introduce uncertainty (RPA vs agentic AI, Wikipedia). In a claims process, you want the agent to decide which claims need extra review, but you want a bot to actually update the system.

Imagine the Scenario: Auto Claims at Scale

Let's make it real. You're a claims manager, and your team handles 1,000 claims a day. Right now, your RPA bots extract data from claim forms, check policy details, and update the claims database. They work 24/7, and they're great at the grunt work (UiPath). But they can't handle a nuanced question like, 'Does this claim need a fraud check?' That's where an agent comes in. The agent reads the claim, decides if it's routine or complex, and then—here's the key—it hands the routine ones to the RPA bot to execute, and escalates the complex ones to a human.

Why RPA Under Agents Wins

This isn't theoretical. IBM cites a multi-agent legal research assistant that routed queries through a low-cost classifier first, escalating only complex cases—cutting contract review time from 90 minutes to 45 minutes (IBM – AI agents). That's a 50% reduction, and it's the same pattern: let the agent think, let the bot do. RPA is the execution layer; it's what makes the agent's plan tangible (UiPath – Phase 3: Agentic Automation). If you try to replace RPA with agents, you'll end up with an agent that can reason but can't reliably click the right button in your legacy claims system.

The Hybrid Architecture: A Practical Blueprint

So how do you actually build this? Start with your existing RPA bots. They're your execution layer. Add an AI agent on top that uses tool calling to decide which bot to trigger and in what order. The agent can also use external tools—like a fraud database—to gather info before it decides. And don't forget human-in-the-loop checkpoints: the agent flags a claim for review, and a human approves it before the bot finalizes payment (IBM – HITL, iterative refinement). That's not a nice-to-have; it's essential. The fact base says organizational change and human oversight, not technology, are the biggest barriers to AI automation adoption (RPA vs agentic AI, Wikipedia). So plan for the human piece from day one.

Comparing Options: Replace vs. Layer

Let's put the two options side by side.

For a claims department, the choice is clear. RPA alone is brittle, agents alone are unpredictable. Together, they're a powerhouse.

Start Small, but Start Now

Here's my specific recommendation: pick one high-volume, rule-based process—like claims data entry—and layer an agent on top that triages exceptions. Use the 90-to-45-minute example as your benchmark: aim to cut cycle time by half. Set up human-in-the-loop checkpoints for anything the agent flags as uncertain. And remember, the market is moving fast: the global RPA market was estimated at $4.68 billion in 2025 and is projected to reach $35.84 billion by 2033 (Grand View Research). That growth is driven by exactly this hybrid approach. Don't wait for the perfect platform. Start with a pilot, measure the cycle time, and let the results speak.

The One Thing to Remember

If you take away just one thing: AI agents and RPA are not either/or—they're a stack. Put the agents on top to think, and keep the bots underneath to act.

Sources

• Robotic process automation – Wikipedia - https://en.wikipedia.org/wiki/Robotic_process_automation
• IBM – AI agents - https://www.ibm.com/think/topics/ai-agents
• UiPath – RPA - https://www.uipath.com/rpa/robotic-process-automation
• Grand View Research – RPA market - https://www.grandviewresearch.com/industry-analysis/robotic-process-automation-rpa-market