How Enterprises Can Manage the Real Risks of Agentic AI

Organizations worldwide are deploying agentic AI across IT, HR, finance, sales, and healthcare. Few are taking equal steps to establish governance maturity with clear boundaries and robust audit mechanisms.

A Deloitte survey of 3,235 IT and business leaders from 24 countries reveals that only 21% say their organizations have a mature governance model in place for agentic AI.

Addressing this gap is urgent. A single agent error can potentially trigger a chain reaction of failures across the enterprise, compounding across systems to turn initial data exposure into a compliance violation and broader organizational liability.

This article covers five categories of agentic AI risks and why organizations should build governance into agent architecture from the start, not after.

What is agentic AI, and why does it introduce new risks?

Agentic AI describes AI systems that autonomously understand goals, reason over context to develop a plan, and take action across systems to complete tasks without requiring human intervention at every step.

These systems comprise a network of autonomous software components called agents, each specialized for a specific task (approval routing, data updates, information retrieval). Working like a team of intelligent digital workers, these agents collaborate across multiple tools, APIs, and data sources.

By orchestrating multiple agents together, agentic AI systems move beyond answering questions to executing independent, multi-step actions without continuous scripting or manual oversight.

The autonomy of agentic AI enables systems to do more than basic automation tools. But that same autonomy creates new risk categories that traditional AI governance frameworks were not designed to address. 

The most significant is the potential for cascading failures. The more steps an agent takes without oversight, the harder it becomes to isolate errors, trace unauthorized actions, or reverse mistakes before they trigger downstream failures.

5 potential risks of agentic AI that enterprise leaders should know

While agentic AI can accomplish far more than basic automation like chatbots, copilots, or single-agent systems, its superior capabilities come with distinct considerations for security and governance.

Enterprise leaders who understand these five categories of agentic AI risks will likely be better positioned to adopt an agentic AI security framework that supports agentic governance and helps them realize business value while managing risk exposure.

1. Hallucinations can compound across multi-step workflows

With single-turn AI, where the system responds to one request at a time, hallucinations are problematic but often contained. Once identified, the error can be corrected immediately without lasting effects on the workflow.

In agentic workflows, an unnoticed hallucination can impact the entire system.

Without oversight at every step, a flawed output may go unnoticed and feed into the next step, then the next. What begins as a small inaccuracy can compound into widespread data corruption, an unauthorized approval, or a compliance violation later in the workflow.

2. Scope creep when agents exceed intended boundaries

Scope creep occurs when an agent takes action beyond its authorized scope, such as an IT agent accessing finance data, changing HR records, or requesting elevated permissions it shouldn’t have.

Policy enforcement is supposed to constrain agent behavior, but it needs proper implementation. If governance is applied via external monitoring or post-hoc controls rather than embedded in the decision layer, scope violations are more likely to go undetected until they cause a compliance breach or security incident.

3. Cascading failures in multi-agent systems

AI agents work together to share information and pursue workflows. When one agent encounters issues, it can impact the rest of the system.

Whether multiple agents operate in sequence or across interconnected systems, when one agent miscalculates, returns incorrect data, or makes another mistake, it can potentially trigger unintended consequences for other agents across systems.

This risk is heightened by agentic autonomy. With no human checkpoint linking agent-to-agent actions, small errors are more likely to propagate.

4. Data exposure through untraceable agent actions

When AI agents query multiple data sources to complete tasks, such as retrieving or updating employee HR records, they may accidentally surface or transmit sensitive information without a clear audit trail.

Audit trails are essential for accountability and, for organizations subject to GDPR, HIPAA, or SOC 2 Type II audits, they are critical requirements. Without traceability, noncompliance and regulatory penalties become possible.

If you can’t trace which data an agent accessed, which systems it touched, and why it took a particular action, it’s a good indication you lack a clear audit trail and won’t be able to defend agent actions in an audit or regulatory investigation.

5. Over-automation of human judgment

Agentic AI can automate repetitive tasks to eliminate tedious work and free teams for more strategic activities. However, not all workflows should run entirely without human intervention, especially in sensitive domains like finance, healthcare, or law, where human decision-making is ethically important or legally required.

One study on human-machine collaboration in credit evaluations found that human involvement was associated with lower default rates for borrowers compared to machine-only decisions. This suggests that high-stakes domains can benefit from mandatory human-in-the-loop checkpoints, where an agent cannot proceed without explicit human authorization.

How to manage agentic AI risks with governance by design

Some organizations build an entire agentic AI rollout and then attempt to add governance as a final step. But strong governance can’t be retrofitted for agentic AI.

Agentic governance should be recognized as a foundational architecture principle rather than an additional layer added after deployment.

An effective agentic AI risk management strategy should include:

• Role-based access controls to limit agent activity
• Policy enforcement at the decision layer to help prevent noncompliant actions before execution
• Decision logs and monitoring for continuous auditability of every decision and action

McKinsey’s playbook offers guidance to help you assess readiness, mitigate risks, and prepare enterprise governance for agentic AI adoption. By embedding risk controls into agent architecture before deployment, organizations may be better positioned for audits and compliance reviews compared to those that add guardrails after deployment.

When you’re ready to get into the practical details, the NIST AI Risk Management Framework is a foundational resource for orgs developing an agentic governance program.

Build governance into agent architecture, not around it

Governance-by-design means building systems so policy enforcement occurs at the agent decision layer, before execution. Every time an agent prepares to take action — accessing data, updating a record, or triggering an approval — the action is evaluated against organizational policies in that moment, not after the fact.

The alternative is relying on external monitoring tools to review agent actions after they're already executed. This approach leaves organizations at risk of missing real-time problems and only catching violations after they occur.

How Moveworks addresses agentic AI risks

Moveworks takes a three-pronged approach to agentic AI risks: pre-execution policy enforcement, governed access and approvals, and complete auditability.

Here's how these mechanisms work together:

• Policy enforcement: Combining intelligent search across governance data, rules, and compliance frameworks with governed action, agents evaluate every action against organizational policies before executing, helping proactively prevent violations rather than detecting them after the fact. With this integration, agents can both retrieve compliance guidance and enforce policy in a single interaction.
• Governed access and approvals: Role-based access controls, least-privilege permissions, and centralized identity management govern agent actions and are designed to route sensitive decisions to a human for authorization when required for sensitive decisions.
• Auditability: Every agent action and decision produces a detailed audit trail, providing evidence to support audit readiness and help meet applicable regulatory obligations.

Underlying all three are built-in content moderation and fact verification to help reduce hallucinations and limit error propagation across workflows.

By bringing these controls together in one platform, Moveworks can support agentic AI deployment within your policies, approvals, and audit requirements, so you can benefit from greater autonomy while maintaining governance.

Schedule a demo to see how Moveworks can support your agentic AI strategy.