AI agents are causing failures that companies still cannot track

Did you know there is a type of production incident that engineering teams are not monitoring yet? This happens because it doesn't fit into any failure analysis model we know. Here's the issue: an AI agent takes an action that seems correct based on its context, but if the context is incomplete, the entire infrastructure can be affected. When reviewing the incident, three different teams might argue whether the failure was caused by the agent or the infrastructure, because we never connected these two concepts.

The problem is real and no longer just theoretical. Today, a large portion of organizations already use some type of AI agent in production, and many plan to expand this. This leads us to a crucial point no one is capturing: failures that occur amid these numbers. Agents are working, they are not cancelled, but they are generating events in the infrastructure that no one has classified as a risk.

I have experience building large-scale infrastructure automation systems, starting at Cisco and later at Splunk. During this time, I patented an intent-based chaos engineering methodology. And you know what I saw repeatedly? Companies treating autonomous agents and chaos engineering as separate disciplines. They are not. This separation is creating the next wave of major production incidents.

The decision agents don't make

To understand why this is important, we need to see what's broken in enterprise chaos governance before adding agents to the equation. Mature engineering teams have already invested in chaos engineering programs, with simulations and impact control. When a human engineer starts a chaos experiment, they make a critical assessment: they check if the system can absorb the disruption at that moment. They check dashboards, analyze error budget consumption, and assess if dependencies are stable. It's not perfect, but at least someone asks the right question before taking any action.

Now, introduce an autonomous agent that can restart services, redirect traffic, or automatically scale resources. That human questioning disappears. The agent sees an anomaly and acts. That action is a chaos event. There is no checking of SLO consumption, impact calculation, or human judgment about whether that is the best time to stress the system, which might already be overloaded.