Human Oversight and Guardrails Are Essential for AI Governance
Traditional AI follows a fairly direct path from input to output. With agentic AI, many steps, systems and decisions can sit in between, so you can’t take the output at face value.
Organizations need visibility into what an agent is doing: Where did it go? What data did it access? What did it change? Did it stay within its assigned boundaries? And what happened once the task was complete?
This is where I see the value of an AI steward.
Financial institutions already have data stewards responsible for data quality, integrity and provenance. As AI becomes more autonomous, they need people who provide the same kind of oversight for AI systems.
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An AI steward doesn’t have to be a single person. It could be a group with deep knowledge of a particular business process. Their role is to confirm that an agent delivers the expected outcomes and to flag anything that looks off.
Here, human judgment remains critical. Machines can process enormous amounts of information, but they lack the ethics, business context and organizational priorities that experienced employees bring.
Organizations should also set measurable risk and performance indicators for their agents so they know quickly if an agent starts operating outside expected boundaries.
That’s the idea behind failing fast. Rather than trying to prevent every failure, build the visibility and controls necessary to catch failures early, learn from them and fix them before they grow.
Put Financial AI Agents to Work Without Creating New Risks
AI discussions often stress keeping a “human in the loop.” That matters, but with agentic AI, putting a human in every step of an automated process defeats the purpose. It just moves the bottleneck instead of removing it.
Instead, organizations should find processes where human involvement mainly slows things down, then consider whether those employees could become the people overseeing the automation.
Institutional knowledge is especially valuable here. An employee who has performed a process manually for years knows what a good outcome looks like, which makes them a strong candidate to be an AI steward. Rather than executing every step, they can monitor the agent, set expectations and step in when necessary.
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Organizations can build in pause buttons, limiters and wait states that require an agent to stop when it hits a predefined condition. The steward can then review the situation, decide whether the agent needs to be corrected and let the process continue.
But the stewards shouldn’t write the rules themselves. A governance group or board should set the policies and taxonomy that define the guardrails, which can then be enforced and continuously monitored using technology. This matters especially in financial services, where agentic AI can create risks that cross organizational boundaries. For example, an agent with access to a cloud environment could launch workloads that generate enormous, unexpected costs.
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The upside is just as large. Agentic AI can combine historical information, institutional knowledge and near real-time data faster than humans can. Financial institutions could use that to improve forecasting, anticipate shifts in demand and spot emerging trends in lending, credit and financial markets.
The organizations that benefit most will be those that learn how to deploy AI responsibly.
Financial institutions should start now by identifying potential agentic AI use cases, mapping the business processes involved, determining where human oversight adds value and setting the policies that will govern autonomous action.
This isn’t just an IT initiative. It touches security, compliance, finance, operations and workforce strategy — and, ultimately, the boardroom.
This article is part of BizTech's EquITy blog series.
