What are the top risks of deploying agentic AI in the enterprise?

The most significant risks in deploying agentic AI center on the fact that these systems do not just generate text, they take actions, and that shift changes what can go wrong in meaningful ways. Prompt injection remains a top concern, but for agentic systems the consequence is not a bad response, it is an unintended action carried out through whatever tools the agent has access to. Tool misuse is closely related, referring to situations where an agent takes a destructive or unauthorized action using a tool it was granted access to for a legitimate purpose.

Data leakage represents another major risk category, occurring either through model outputs that inadvertently reveal sensitive information or through third party integrations an agent connects to as part of completing its tasks. Cascading failures present a risk that is somewhat unique to agentic and multi agent systems, where a single compromised or manipulated agent can affect the behavior of other agents it interacts with, spreading a problem well beyond its original point of origin.

What distinguishes these AI risks from traditional software risk is that they often cannot be caught through static code review, since the vulnerability is not in the code itself but in how the agent behaves given certain inputs or situations. This requires behavioral testing that actively probes for these failure modes and runtime monitoring that can catch problematic behavior as it occurs in production. Akto's Argus is built to continuously red team and monitor specifically for these agentic risk patterns, testing the kinds of scenarios that traditional security review would not typically surface.