AI Isn’t One and Done: Banks Need an Agent Admin Before They Need Another Model

Key Takeaways From This Blog:

• AI isn’t static software, it learns, drifts, and changes over time. Treating it like a plug-and-play tool is a mistake. Organizations that fail to actively monitor and adapt their AI systems will quickly fall behind or introduce unseen risks.
• We’ve moved beyond simple chatbots. AI agents now act, decide, and transact,  and enterprises are orchestrating multiple models based on speed, cost, and accuracy. Intelligence is no longer a single product; it’s a dynamic marketplace.
• As AI systems evolve independently, organizations must create a role to oversee them, the Agent Admin. This person monitors model drift, reviews output quality, manages routing logic, and ensures compliance. Without this oversight, systems can develop “bad habits” unnoticed.
• The winning strategy isn’t picking the biggest AI vendor; it’s building the operational spine that connects models, data, and governance. Falling model costs free up resources that should be invested in oversight and explainability, not more tools.
  
  

AI moves like current through an exposed wire. What looked groundbreaking six months ago now feels routine. Model costs have plunged twentyfold. Capabilities have multiplied. Yet many institutions still treat AI like static software. They buy it, plug it in, and walk away.

That thinking will age faster than the tech itself.

The Era of Agents

Technology companies have shifted from chatbots to autonomous agents. These agents don’t just answer questions. They act. They decide. They transact.

Enterprises now build systems that route every task to the model best suited for it, evaluating speed, cost, and accuracy on the fly. Intelligence is becoming a marketplace, not a monolith.

The good news is that the price to use these models has dropped to fractions of a cent. The bad news is that errors are cheaper, too.

My Own Wake-Up Call

For the past six months, I have used my own AI agent as a kind of digital right hand. It has drafted frameworks, summarized research, and helped structure early ideas before they became strategy pieces.

It has been fantastic. Reliable. Consistent.

Until this week.

The responses started to drift. The phrasing changed. The insights weren’t as sharp. I could feel it thinking differently.

Now I have to dig in and see what changed inside it. What new data shaped its tone? What updates altered its logic? Somewhere along the way, it learned something new and forgot something useful.

That’s not a failure. It is the reality of a system that keeps learning when you are not looking.

And it is exactly why institutions need an Agent Admin. Someone responsible for checking what the agents have learned, what they have unlearned, and how their reasoning is shifting.

The Hidden Cost of “Set and Forget”

AI doesn’t break like software. It erodes. It drifts. It adapts in directions no one authorized. When you rely on one model without oversight, you inherit its blind spots and its new bad habits.

Smart organizations are building roles that manage this drift. These Agent Admins review output, compare model performance, adjust routing logic, and retire agents that are starting to lose accuracy. They treat AI like a workforce that requires supervision, not a product that can self-regulate.

What Slower Adopters Should Know

If your institution is still debating which vendor to choose, you are behind. The real work is orchestration, not procurement.

Three truths stand out.

1. AI is an ecosystem, not a contract. The advantage lies in routing to the right model for the right task, not in signing with the biggest name.
2. Falling costs do not mean falling oversight. Every dollar saved should fund governance, version control, and explainability.
3. The real value is orchestration. Accuracy depends on how you connect models to your data and people, not on which AI brand you use.

The Rise of the Agent Admin

Every major system inside your institution already has an administrator. The core. The CRM. The loan platform. Soon, you will need one for AI.

This person defines what tasks are safe for automation. They decide which models interact with which data. They monitor drift, measure cost per answer, and track output quality. They are the line between speed and chaos.

I now think of my own experience with that drifting agent as a warning. Without oversight, even a helpful system can start to invent its own logic.

The Strategy Shift

You do not need more AI vendors. You need an operational spine for AI.

Clean data. Routing rules. Evaluation scripts. Governance that treats AI like a new employee who reports to Compliance on day one.

Once that foundation exists, you stop asking which model to buy and start asking how to keep your models aligned with the truth.

The Takeaway

AI evolves without asking permission. The institutions that survive will be those that treat it like a living system to be guided, not a finished product to be installed.

The smartest hire of 2025 will not be another data scientist. It will be the Agent Admin who keeps the machines accountable.

AI does not wait for a policy review. It keeps learning while you are in a meeting.