I’m a credit card transactor – I pay off my balance in full each month and use credit purely as a payment method to avoid interest.

Recently, a major payment processor experienced an outage that disrupted many money movement services. As a result, my scheduled electronic payment was converted into a mailed check to my credit card company. I worried this would delay my payment – and unfortunately, it did. I was charged a late fee and interest as soon as the check was processed.

I wasn’t happy and I immediately called my credit card company. After verifying my identity and saying just four words – “late fee and interest” – to the interactive voice response (IVR) system, I heard a few seconds of clicking. Then the system responded: “We will reimburse your late fee and interest charges. You should see those transactions in two business days. Anything else you need help with? If not, you can hang up.”

From start to finish, the call took less than two minutes. I never spoke to a human, yet my issue was resolved.

This real-life example illustrates the power of agentic AI and one of its many practical use cases. But what exactly is agentic AI and how does it work?

Agentic AI in a nutshell

Until now, AI has primarily assisted humans by offering insights. Generative AI (GenAI) takes this further by creating original content in response to prompts or instructions. Agentic AI represents the next step – it makes decisions. This ability to act independently is what sets agentic AI apart from traditional AI.

As agentic AI systems gain autonomy, their capacity to make informed, strategic decisions at scale becomes mission critical. These AI agents can:

• Research, analyze and synthesize information.
• Execute tasks across multiple domains.
• Collaborate with other AI agents to solve complex problems.
• Adapt and optimize workflows without human input.

How agentic AI works

Agentic AI operates through a continuous feedback loop of perception, reasoning and action with or without human involvement. These systems gather data from their environment, interpret context using machine learning models and make decisions aligned with specific goals.

What sets agentic AI apart is its ability to act autonomously, adapt based on outcomes and refine its behavior over time. It often relies on planning algorithms, reinforcement learning, or goal-oriented frameworks to navigate complex tasks. This makes agentic AI especially effective in dynamic environments where real-time decision-making and adaptability are essential.

By enabling systems to operate independently and learn from experience, agentic AI continuously improves performance and responsiveness.

From reactive to adaptive with agentic AI

Banking is poised to benefit significantly from agentic AI. Traditionally, the industry has relied on rigid legacy systems to structure business processes – systems that have often hindered modernization. Agentic AI presents a transformative opportunity to shift from reactive, legacy-bound operations to adaptive, intelligent services.

This shift paves the way for more accessible, personalized banking experiences and greater employee efficiency. In the past, complex workflows required numerous human touchpoints, outdated processes and multiple handoffs between teams. Agentic AI can streamline these interactions, reducing friction and complexity while enabling seamless, end-to-end customer experiences.

Agentic AI in the banking industry

A perfect example of agentic AI in banking comes from my recent customer service experience. I expected a long call with my credit card company, navigating the usual IVR maze and almost certainly the need to explain my situation to a human.

Instead, the AI agent exceeded my expectations and in record time to boot. You might even say it was an ideal and almost entirely frictionless customer experience.

Now I’m not suggesting you make a late payment just to test this out, of course. But if you’re a bank? You should absolutely be thinking about what AI agents can do for your business. What else can AI and data do for banks? Check out AI in Banking.

Learn more

Tune in to the upcoming SAS webinar Beyond the Buzzword: Agentic AI in Financial Services to hear from Stephen Greer, Banking Industry Consultant, and Marinela Profi, Global AI Strategy Leader for AI Agents and Generative AI, as they explore the real-world impact of intelligent agents in the industry. And stay informed by following the latest blog posts from SAS on agentic AI.