Implementation Approach to Agentic AI in Banking

For banks, implementing agentic AI is not simply a matter of finding a process to automate and putting an agent to work. The real challenge is building the foundation on which that agent can be trusted to take action.

That requires banks to think carefully about what comes before, during, and after deployment. Here are four areas banking leaders should focus on as they move from experimenting with AI agents to putting them to work in real business processes.

Get the Foundation Right Before Adding AI

Once banks identify a promising use case, there can be a temptation to move straight to implementation. But before introducing an AI agent, it's important to look at what is already happening and how this process works today. If the foundational workflow isn't working properly, adding AI on top of it isn't going to fix the underlying problem.

That's why getting the base or foundation right first is so important. Banks need to fully understand the process that the agent is going to operate within and make sure it will sufficiently support the proposed additions before introducing another layer of automation or autonomy.

It's tempting to try to bolt AI on top of existing solutions because that's the new capability. But the agent still needs support from a solid underlying foundation. If that foundation isn't there, it becomes much more difficult to isolate and evaluate agent performance, and whether the agent itself is working properly or it is simply operating within a process that already has problems.

Build Governance In From the Beginning

Alongside the workflow itself, banks must think about governance from the start. If an AI agent is going to take actions within a business process, the bank should understand what it is doing, when it is doing it, and why it is doing it.  Otherwise, there is no visibility, potential for improvement, or risk mitigation opportunity.

That means building in an auditable approach from the beginning. Banks should be able to see whether the agent is acting at the right time and in the right scenarios, particularly as it starts taking on more responsibility.

Governance is also an important part of establishing and building trust in the agent. Before allowing an agent to do more, the bank must have confidence that it can consistently do what is expected of it. So governance isn't something that comes after implementation; it's a fundamental part of building, implementing, deploying, and expanding the agent over time.

Take a Crawl-Walk-Run Approach

Not everyone is ready to fully hand over the keys to an AI Agent. So rather than trying to automate too much at once, some banks prefer to follow a crawl-walk-run approach, which allows institutions to introduce agentic AI more gradually. This is a proven method to increasing trust and adoption in the new AI capabilities being introduced.

First, start with a manageable, low-stakes use case (example: Account Summary Agent).  Document and analyze how the agent performs, and make sure the right governance is in place. Then expand from there. 

Expansion could include allowing the original agent to take on more autonomous activities or access additional data sources.  It could mean expanding to other business units within the bank.  Or it could mean introducing additional agents to handle other use cases. As the institution becomes more comfortable with AI over time, all of these would be expected.

The crawl-walk-run approach is successful because there is a real difference between demonstrating that an agent can perform a task and trusting that agent to perform it consistently as part of a real banking process. That trust has to be built over time, and a phased approach allows the institution to learn and improve before increasing the level of autonomy.

Keep Measuring How the Agent Performs

Once an agent is up and running, banks should also continuously monitor its performance, particularly as the agent learns new skills and starts taking on more responsibility. 

Dashboards, reports, and audit trails can help teams identify where an agent is performing well and where it may not be. From there, the institution can pinpoint areas of improvement and continue training and fine-tuning the agent. Many of the analytics should tie back to the KPIs the bank already tracks, and especially the ones that were agreed upon related to the agent. The idea isn't just to know that an agent completed an action or how much time was saved, it is to ensure that the agent is staying within its guardrails, performing the actions it is supposed to, and providing valuable results.

In practice, this becomes an ongoing cycle: define scope, goals, and KPIs, implement the agent, measure performance, identify improvement areas, and train to fine-tune those areas. The agent shouldn't be treated as something that is deployed once and then left alone. Its performance and value must be monitored and improved as its role grows. Continuous improvement cycles directly correlate to continuously increasing value of agents.

Final Thoughts

Implementing agentic AI is not about trying to automate as much as possible, as quickly as possible. Banks must be confident that an agent can the tasks it is designed to do (and only those tasks), at the right time and in the right scenarios.

That starts with establishing an optimal foundation for the agent to work with, and building in governance from the beginning. From there, banks can take a phased approach: start with a manageable use case, evaluate the agent’s performance, identify and address improvement areas, and continuously train and improve it with new skills and responsibilities. 

Keep track of the KPIs that matter most, and use those insights to maximize the value of the agent. The more banks can prove that their agents are delivering the intended results, the more confidently they can scale agentic execution across banking workflows.