Agentic AI Presents Risk at a Scale Never Before Seen

Agentic AI is the next step for AI technology. AI agents are currently at the cutting edge, and that makes them dangerous. We’ve previously written about many of these risks,  ranging from errant commerce bots to “agent washing.” However, what makes agentic AI so risky isn’t only its ability to act without human intervention, but its ability to do so at scale. Humans can only move so quickly, that’s both a flaw and a feature. If a human makes an error, there’s a good chance that person or others catch it and prevent the next error before it happens. Agentic AI systems can act autonomously and much faster than humans, making mistakes just as quickly, before anyone can intervene. A recent Venable memo dives into this concept:

“Decisions can be operationalized instantly and executed at significant scale. Conduct that once required repeated human involvement can now be carried out through automated systems with minimal additional input. In that setting, ethical and legal constraints risk being displaced by a single instruction. The consequence is not only acceleration, but amplification. What would have been one decision can become hundreds or thousands of actions, each traceable to the same originating choice, without the intervening friction that might otherwise prompt reconsideration. This scale alters the nature of risk, as it involves more than just a single employee acting improperly. Instead, it is the potential for distributed conduct, executed rapidly and consistently across systems, with each action creating a record.”

This ability to make mistakes at scale means mitigation and guardrails are paramount. Luckily, the memo provides us with some tips:

• “Technical guardrails, including sandboxed environments, API restrictions, and network controls
• Identity and access management frameworks assign discrete identities to agents and enforce least-privilege principles, ensuring that agents operate only within narrowly defined scopes
• Continuous monitoring and logging provide visibility into agent behavior, creating audit trails that support both internal oversight and external accountability
• Human-in-the-loop controls introduce checkpoints for high-risk actions, requiring affirmative approval before an agent may execute decisions with legal or financial consequences
• Life-cycle management processes govern the deployment, updating, and retirement of agents, ensuring that systems do not “drift” in some way, persist beyond their intended use, or operate under outdated assumptions.”