Deploying AI? Invest in Model Risk Management

What Is Model Risk Management?

Model risk management (MRM) is the process of identifying, assessing, and mitigating risks associated with using financial, statistical, or machine learning models.

MRM ensures models are accurate, reliable, and aligned with regulatory requirements. Key steps include model validation, monitoring, and governance throughout a model's life cycle.

What Does a Model Risk Manager Actually Do?

The model risk manager ensures the models are accurate, compliant, and fit for the business purpose.

They identify risks, test model performance, monitor for drift, and ensure proper documentation for the model’s actions and decisions. Besides accuracy, MLMs also assess for fairness, robustness, and explainability as AI gets more complex.

They can run scenario tests, stress-test outputs, and ensure models can be audited.

It’s also important to note that they often collaborate with data scientists, engineers, regulators, and business leads to keep AI aligned with the business goals and governance standards.

Why Is Model Risk Management So Important for AI?

AI models don’t only make predictions and generate insights, they also make decisions. When they are wrong, the consequences can be very serious, including hallucinations, bias, drift, black-box outputs, and regulatory fines.

Industries like finance, insurance, and healthcare face the highest stakes.

A flawed AI model can lead to denied loans, incorrect claims processing, or even harmful treatment recommendations.

Model risks, which can snowball over time, often come from:

• poor data quality
• lack of transparency
• flawed assumptions

That’s where Agentic AI platforms like AgentFlow are helpful. Since AgentFlow allows you to use your own data, it reduces the risk of relying on unvetted or irrelevant data sources.

A good MRM isn’t just a compliance checkbox.

It’s an important part of the AI implementation process to keep your AI trustworthy, fair, transparent, and aligned with your business goals.

Model Risk Management in the Age of AI Agents

AI Agents aren’t just models anymore. They’re autonomous systems that can observe, decide, and act.

They’re powered by models, memory, and goals, so they adapt to their environment and take multi-step decisions and actions to achieve their goals.

While all of this is good, that’s also where the risks multiply.

Unlike traditional models that used to make one-off predictions and insights, AI Agents can now make ongoing decisions. They can change behavior based on new inputs, learn from past actions, and operate with limited oversight.

That’s where it gets tricky, as you’re no longer only managing model accuracy. Instead, you have to manage the downstream impact of autonomous AI Agents and their actions.

A traditional model risk management framework is built for static models, and they’re struggling to keep up.

Therefore, for Agentic AI, model risk assessment must be continuous, adaptive, and fast enough to handle the evolving AI in real-time.

Mario DiCaro from Tokio Marine HCC Insurance Holdings shared similar thoughts on our podcast. He also mentioned that as AI continues to develop, the role in risk assessment and AI in financial modeling will likely grow.

Here are the best practices to manage your Agentic AI.

Model Risk Management for Agentic AI: Best Practices

AI Agents are taking more autonomous roles, so managing their risk requires more than just testing the underlying models.

We recommend having a framework that accounts for continuous actions, evolving goals, and the potential for unintended consequences.

Therefore, here are the best practices to take when managing model risk for Agentic AI:

Continuous Monitoring

AI Agents operate in loops since they can make decisions, observe outcomes, and learn from the outcomes in real-time. Unlike traditional static models, this makes continuous model monitoring a requirement.

You need a continuous system that can flag unusual behavior when it happens (such as deviation from expected outputs or performance drop caused by data drift).

Periodic checks don’t cut it when actions are happening 24/7 with Agentic AI.

Scenario Testing

As Agentic AI operates in a dynamic environment, unexpected inputs can lead to unexpected actions.

Traditional tests don’t cover the full spectrum of real-world complexity, so including scenario testing is a good way to understand how AI Agents operate under pressure.

Scenario tests like simulated edge cases, stress environments, and adversarial inputs can help uncover hidden failure modes before they appear in production.

Explainability

When AI Agents take action, accountability becomes critical.

It’s important to know why an Agent made a decision, whether it’s for compliance, debugging, or trust-building. This is especially challenging with black-box models.

That’s why we addressed this with AgentFlow, which tracks and logs every decision, input, and outcome. This makes Agent behavior transparent, reviewable, and explainable, even after deployment.

Human-In-The-Loop

Not every decision is for the AI Agent to make. In high-risk situations, human oversight can be the safety net that prevents harm, but it only works if it’s built in.

With AgentFlow, we enable human-in-the-loop orchestration, which allows seamless collaboration between AI Agents, third-party systems, and human reviewers.

For example, you can define exactly when humans intervene, whether it’s to approve, reject, or adjust the Agent’s course of actions.

Dynamic Risk Assessment

Agentic AI systems aren’t static because they adapt over time, so their goals might shift, their training data might change, and their environment may evolve.

That’s one of the reasons why one-time risk assessment is outdated the moment an AI Agent begins to learn.

MRM for Agentic AI requires dynamic assessment, a continuous process that re-evaluates risk as conditions and behaviors change.

Clear Boundaries

Autonomy without limits is a liability.

That’s why it’s important to define strict operational limits, what an AI Agent can and cannot do, which system it can access, and what thresholds trigger human review or shutdown.

Clear boundaries help contain the impact of errors, reduce the regulatory risk, and keep AI Agent behavior aligned with business goals.

Need Help With AI Deployment and Model Risk Management?

We help businesses safely deploy Agentic AI with built-in model risk management, human-in-the-loop orchestration, and transparent decision tracking. With our Agentic AI platform, AgentFlow, you can make, manage, orchestrate, and monitor AI Agents with a security-first approach.

To learn more and see AgentFlow live in action, please book a free demo with our experts and see how Agentic AI can help your business.