How do I transition from traditional RPA to Agentic AI workflows in a FinTech environment?

All answers to this question.

Transitioning from RPA to Agentic AI is a game-changer for FinTech. While RPA is great for "doing," Agentic AI is designed for "thinking." In 2024, we’re seeing a shift toward frameworks like AutoGen or CrewAI where agents have specific roles—one for data retrieval, another for risk assessment, and a third for final verification. Unlike scripts, these agents use LLMs to reason through anomalies. You’ll want to start by mapping your most frequent "human-in-the-loop" triggers and seeing if an agent can handle the reasoning logic using a RAG-based knowledge source.

   Answered 2024-05-16 by Jennifer Miller

Have you looked into the Model Context Protocol (MCP) for integrating your existing legacy databases with these new autonomous agents? It might simplify how they pull transaction history.

   Answered 2024-05-18 by Robert Thompson

• Robert, that’s a great point. MCP is becoming a standard for a reason. By using MCP, your agents can securely query your SQL databases or even third-party APIs without you having to write custom connectors for every single tool. This significantly reduces the latency in fraud detection loops and makes the "agentic" part of the system much more proactive rather than reactive.

     Commented 2024-05-19 by Sarah Jenkins

Start with a hybrid approach. Keep RPA for the high-volume, simple data entry, and layer an AI agent on top to handle the exceptions that currently require a human's eyes.

   Answered 2024-05-20 by David Wilson

• I totally agree with David. A hybrid model is the safest way to scale. It allows you to maintain the speed of RPA while leveraging the cognitive flexibility of Agentic AI for complex cases.

     Commented 2024-05-21 by Jennifer Miller