If you don’t already know what it is, you’ve probably heard the term: AI Agent.

Flashy. Sounds super secret. A little bit of Bond meets Mission Impossible, but inside The Matrix.

But what is an AI agent, really? What can it do for credit unions? And… are they worth paying attention to (yet)?

Let’s dig in.

What Is an AI Agent?

An AI agent is a software program that can perform tasks or make decisions on its own. It usually works toward a goal, and it uses data, logic, and sometimes trial-and-error to get there. It can analyze a situation, decide what to do next, and take steps to complete a task.

AI agents like automated assistants. I could just pull up an account balance, sure. Or it might recognize when a member is likely to ask about a suspicious charge and proactively offer support options. Or it might detect that a home equity line of credit is open but unused and offer that member some information about how they might put it to use.

AI agents are built on newer AI models—often large language models like GPT—but they can do quite a bit more. They act. And increasingly, they can do so without supervision.

How Do AI Agents Work?

Most AI agents start with a goal, a list of possible actions, a memory or way to track past steps, and access to tools or data. Give them a goal, and they’ll try to achieve it by choosing the best available actions. That could mean anything from pulling transaction data to filling out a form to sending an alert.

In a credit union setting, this might mean listening to a conversation in the call center, identifying the member’s intent, and triggering a series of back-office processes. One agent might verify identity. Another might pull up relevant documents. A third might update the CRM. Some agents are specialized, and others are generalists that can call on specialized tools when needed.

They’re still early tech, so most of the time you’ll see AI agents doing simple, high-volume tasks like pre-qual checks, balance transfers, or FAQ-style member support.

But that’s changing. Many can string together multiple steps to handle something end-to-end. We’re really staring down the future, where we’ll all be working alongside AI.

What Are AI Agents Used for Today?

Right now, AI agents are being used in a few ways across financial services. The most common place you’ll find them is in call centers and “chatbots,” though “chatbot” might not be the best term anymore.

In fact, most chatbot vendors have resisted the term for years. Makes sense, since so many of the chatbots have evolved—they can finally do more than say, “I don’t understand that.”

Modern chat agents can (usually) fully understand the user… and they can actually complete tasks. Think: processing payments, updating member records, or initiating card disputes.

Some are even handling internal workflows. Need to update a batch of loan records? There are agents that can do that automatically. Others help with training by answering questions from new employees, guiding them through systems, or flagging when they’ve missed something important. They can be good at repetition, and they don’t forget steps.

In a few cases, credit unions are using AI agents to help spot fraud. They monitor transaction patterns, member behavior, and other signals, and they alert human staff only when something looks suspicious.

How Will Credit Unions Use AI Agents in the Future?

Over the next two to three years, you’ll likely see AI agents get more involved in member support, operations, and even marketing. You might see agents that listen to every call, summarize the outcome, and recommend next steps. Or agents that automatically generate personalized offers based on a member’s transaction history and life stage.

One of the more interesting developments is how agents are being trained to work across systems. Right now, a big barrier is integration. But AI agents are getting better at understanding screens, forms, and data, even in legacy systems. That opens the door for agents that can do real work without major tech overhauls.

As long as they have access, they won’t need perfect APIs.

We’ll also start seeing more coordination between agents. One agent handles the member chat. Another handles the backend updates. A third flags the interaction for a follow-up.

This kind of agent teamwork will make it feel less like you’re interacting with a tool and more like you’re dealing with a responsive, intelligent system.

Should Credit Unions Look for AI Agents Now?

That depends on what you want to solve. If you have a high volume of calls with repetitive questions, or if your staff spends too much time jumping between systems to complete simple tasks, then yes, an AI agent could help today.

However, if you feel like you’re well staffed, and aren’t leaving members hanging—or you’re handling training and internal knowledge and tedious workflows with ease… then maybe you can wait.

It’s also fair to be cautious. AI still has question marks around capability, explainability, regulations, ethics and environment, and probably a few more things.

Also, not every AI agent on the market is equal. Some are little more than chatbots with fancy names. Others require a lot of setup or only work well in narrow situations. Before jumping in, it helps to define a single problem you want to solve. Something small, repeatable, and measurable.

From there, it’s easier to evaluate what’s available. You don’t need to build your own, but you can. In fact, if you have complete, accurate content on your website, you can build a good one in a single day. Learn more here.

Next Steps

Even if you want to build your own AI agent, you don’t need a full AI team. In most cases, you’ll just need an internal champion… and maybe partner who understands the credit union ecosystem who can help connect the right tools to the right tasks.

The technology is moving fast, but you don’t need to rush. You just need to get clear on what would actually make life easier for your staff and members.

If you’d like to learn more about how to use AI in your credit union, let’s talk! We’ve been helping a lot of execs tackle everything from AI policies and ChatGPT prompts to best-fit fintech solutions and strategic planning.

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