Agents in the Loop: Rethinking Risk, Compliance and Governance with AI

This article is the third in my four-part series exploring how intelligent agents are reshaping the foundations of fintech operations.

• In Part 1, we looked at why the smartest fintechs are scaling not by growing headcount, but by building with agents. These systems aren’t just automating tasks – they’re giving lean teams enterprise-grade leverage, collapsing toolchains and accelerating execution without adding organisational drag.
  
  

• In Part 2, we explored how to transform data chaos into structured context. Rather than relying on disconnected dashboards, leading fintechs are deploying agents and context graphs to build a live, semantic view of the business. The result is faster decisions and smarter systems.
  

In Part 3, we turn our attention to risk, compliance and governance. As AI agents take on more responsibility – acting, coordinating and learning across workflows – they also introduce new layers of complexity. This piece outlines how fintechs can govern agent behaviour without slowing innovation. From embedded audit trails to oversight agents like the “Judge Agent,” we explore what AI-native compliance looks like and how to design for it from day one.

Speed Without Supervision Is a Risk Multiplier

The most advanced AI agents don’t just answer questions - they write emails, submit reports, trigger workflows, update databases and coordinate across departments. They’re not just handling information; they’re making decisions and taking actions.

But these decisions are often made in milliseconds, based on probabilistic reasoning, shifting context and limited visibility. If an agent misclassifies a regulatory obligation, misroutes a flagged transaction, or skips escalation due to overconfidence, who takes responsibility?

Traditional governance models weren’t built for this. They rely on deterministic systems and human checkpoints. But autonomous agents operate independently and at speed, making post-hoc review ineffective. What’s needed is a new kind of oversight that keeps pace with the systems it governs.

Enter the Judge Agent

One promising approach is the introduction of oversight agents that actively participate in decision-making. A Judge Agent evaluates behaviour, monitors risk thresholds, enforces escalation protocols and ensures decisions stay within acceptable bounds.

Rather than replacing compliance teams, the Judge Agent enhances their capacity by providing consistent, real-time oversight at scale. As a programmable layer of operational judgment, it helps ensure decisions stay aligned with policy, risk thresholds, and regulatory expectations.

For example:

• When a policy update is detected, the Judge Agent identifies affected workflows, informs stakeholders and confirms that required changes have been implemented.
  
  

• If an agent’s confidence score falls below a set threshold, the Judge Agent flags the task for human review before action is taken.
  
  

• When sensitive data is detected in an outbound draft, the Judge Agent can halt the process and request secondary validation.
  

Oversight is no longer a matter of spot-checking results. It becomes a continuous, embedded process.

Governance by Design

Modern governance can’t rely on documentation alone. Policies in PDFs and guidelines buried in onboarding decks may tick regulatory boxes, but they don’t scale with the speed or complexity of autonomous systems. As AI agents begin to operate across workflows, make decisions in real time, and adapt through feedback, governance must evolve from static rules to dynamic enforcement.

That means designing systems where every action is traceable — capturing the inputs used, the confidence levels assigned, and the reasoning behind each decision. It means surfacing decision logic in ways that reveal not just what happened, but why — including which data was used, which constraints applied, and which alternatives were considered. And it means establishing guardrails that actively shape behaviour, detect anomalies, and mitigate risk in ambiguous or high-stakes situations.

With this approach, compliance shifts from reactive oversight to proactive infrastructure. Governance isn’t an add-on — it becomes part of the system’s core architecture, embedded in how decisions are made, risks are managed, and accountability is enforced across the organisation.

A Shift in Regulatory Expectations

Regulators are no longer satisfied with documented intent. They are beginning to require real-time explainability and outcome traceability. That means fintechs must be able to show not only what happened, but why.

It also means that systems need to answer questions like:

• What inputs shaped this decision?
  
  

• What alternatives were considered?
  
  

• How did the agent assess risk?
  
  

• Who, if anyone, validated the outcome?
  
  

This level of scrutiny is fast becoming a baseline expectation. Retrofitting governance is costly and complex. Building it in from the start is faster – and far more sustainable.

From Human Oversight to Agent Collaboration

We’re moving beyond “human-in-the-loop.” In modern systems, agents increasingly monitor, supervise and even audit one another.

This doesn’t remove people from the process. It elevates their role. Humans set strategy and guardrails. Agents carry out execution, coordinate feedback and ensure compliance is respected in real time.

Governance becomes a distributed capability across a network of agents. Some act. Others review. Some flag anomalies. Others log decisions and prepare reports for human review.

The result is not just automation. It is an ecosystem of intelligent oversight.

What Fintech Teams Can Do Today

For teams building or scaling agent infrastructure, here are five concrete steps to prepare for AI-native governance:

1. Define what “good” looks like
   Codify acceptable behaviour, thresholds and edge case protocols.
   
   

2. Instrument your agents
   Log reasoning steps, decisions made and key inputs – in a way humans can audit.
   
   

3. Design escalation logic
   Build in mechanisms for agents to flag uncertainty and request human review.
   
   

4. Separate execution from evaluation
   Use different roles or agents for task completion and outcome review.
   
   

5. Prepare for scrutiny
   Assume regulators, partners and clients will ask tough questions. Your system should be ready to answer

Conclusion: Rethinking Governance – From Oversight to Strategic Advantage

AI agents bring power, speed and scale. But they also raise the stakes. The solution isn’t to slow them down. It’s to build oversight that moves just as fast – and thinks just as clearly.

Fintechs that succeed in the coming wave won’t just automate. They’ll govern with intent, design for transparency and adapt faster because their systems are built to explain themselves.

In this new operating model, governance is not a drag on speed. It’s what makes speed sustainable – and trust scalable.