By Alexandra Mousavizadeh, Co-Founder & CEO, Evident

Agentic AI: Responsible AI is something that the world’s biggest banks take very, very seriously. Because financial services are already so heavily regulated, banks are having to grapple with AI adoption in a way that ensures safety and responsible use, which could become a great model for other industries, setting the blueprint for responsible AI adoption across the board. Regulation aside, banks are risk-averse by nature. Their ability to address the black-box nature of AI systems has been repeatedly tested in recent years against a backdrop of continuous technological advancement and evolving regulation.

In fact, Evident’s recent Responsible AI in Banking report reveals that bank employees with remits related to Responsible AI (RAI) grew by 41% in 2024, while the number of banks publishing RAI principles tripled over the same period.

Leading banks understand that those who invest early and consistently in RAI are not only protecting themselves from future harm, but they’re also gaining a competitive edge, with RAI acting as an accelerant for organisation-wide AI adoption. However, with the arrival of agentic AI, responsible AI is at a reckoning point. Existing RAI challenges – like explaining “black box” model outputs and safeguarding large, complex data sets – have been heightened.

Yet, unlike earlier machine learning systems, which are largely passive and task-specific, agentic AI refers to systems designed to act autonomously – able to perceive their environment, learn from it, make decisions, and take independent action. The very properties that make AI agents so powerful -navigating APIs, engaging directly with customers, and even reprogramming their own behaviour in response to changing contexts – also make them more difficult to govern. These are the core reasons banks must now reinforce their AI guardrails to adopt agentic systems safely, offering valuable lessons for every industry on the same path.

Agentic AI operates beyond traditional controls

Agentic systems challenge the assumptions on which banks’ current governance is built. Most AI oversight protocols – risk registers, validation reviews, explainability standards – are designed around static models operating in tightly scoped environments. Agentic AI, by contrast, is dynamic and distributed. Even when parameters are well-defined, agentic models can self-optimise in unpredictable ways. They’re not just tools – they are actors whose actions require monitoring in real-time, with adaptive guardrails and escalation paths that evolve in line with the agentic system itself.

In practice, this means investing in things like diagnostic questionnaires at the point of use, risk-tiered access controls, and ultimately, AI assurance platforms – integrated systems that monitor and validate AI use across the enterprise before and after products go live. The goal isn’t to constrain innovation. It’s to create the conditions in which innovation can scale safely.

Compliance and security risks are rising

Agentic AI doesn’t just introduce new technical complexity – it raises the stakes for compliance and reputational risk. As banks ramp up the adoption of general-purpose AI tools, regulators are watching closely. The EU AI Act, NIST frameworks, and ISO standards all point to a world where financial institutions will be required to demonstrate not just model performance but also responsible use.

And the risks span far beyond bias in lending algorithms or faulty fraud detection. We’re talking about systemic issues: third- and fourth-party risk from API integrations, cybersecurity vulnerabilities in self-updating agents, and opaque decision-making that erodes consumer trust.

In this environment, banks need to demonstrate active accountability through robust documentation, auditable controls, and explainability mechanisms that can keep up with increasingly complex AI systems. As one of our report contributors put it, “AI risk is no longer just model risk. It’s architectural, ethical, and operational.”

Early action is a strategic advantage

The narrative that RAI slows things down is fundamentally wrong; over the past few years, we’ve observed the opposite to be true. The banks that invest early in RAI are not just mitigating risk but also building capability. Our data shows that the most mature institutions – those with embedded RAI talent, mapped AI controls, and real-time oversight – move faster. They reduce time to production. They accelerate the approval of low-risk use cases. And crucially, they earn trust – internally and externally.

This is particularly salient as agentic AI begins to underpin day-to-day service provision. From lending to wealth management and personalised banking, trust is currency. The ability to demonstrate that your AI agents are explainable, fair, and accountable will define who wins the next wave of competition.

Responsible Agentic AI requires organisational transformation

Agentic AI creates demand for emerging roles, from AI ethicists to machine learning researchers. It will require the integration of assurance platforms, explainability tooling, and dynamic risk assessments into the AI lifecycle. And it will require senior leadership to understand the impact that this technology will have both on customer outcomes and organisational behaviour. The future isn’t about whether banks adopt agentic AI. Banks may be leading the way, but agentic AI is coming for every industry. The real question is: how will we all adopt it responsibly?