AI Agents Could Be the Silent Chaos Creators in Tech Systems

AI agents are the new wildcards in tech infrastructure, quietly stirring the pot in ways many enterprises aren't prepared for. We're past theory. A hefty 79% of organizations already use some form of AI agent, with 96% planning to expand. That's a lot of digital workers making decisions faster than any human could, but are they too fast?

The Invisible Trouble

When an AI agent makes a call, it follows its script. Spot an anomaly, act. Sounds straightforward, right? But here's the catch: The script is blind to the bigger picture. It doesn't know if three other processes are straining the system or if a critical database is already stretched thin. The result? A single corrective action spirals into a full-blown tech meltdown. It's a situation that's more common than you'd think, yet rarely captured in incident reports.

chaos engineering, humans would see the red flags. They'd measure the system's stress tolerance, ask the right questions. Agents don't. They dive in headfirst, often into a storm they can't see coming.

The Missing Link

Why aren't we treating these agents as chaos creators? Because historically, agents and chaos engineering have been siloed. But they shouldn't be. They're two sides of the same coin, and ignoring the link is risking the next big tech incident.

Enter the resilience budget, a dynamic approach that treats system capacity as a consumable resource. Instead of static thresholds, it updates in real-time, factoring in SLO burn rates, latency trends, and other signals. It's the kind of forward-thinking model that our AI-driven world desperately needs.

AI's Role and Its Limits

Some forward-thinking companies are trialing large language models to predict chaos scenarios. They pull from postmortem data to tease out potential failure modes. But there's a glaring limit: these models rely on up-to-date dependency graphs. Fall behind, and you're back to square one, making guesses based on outdated info.

The takeaway? AI can suggest, but it shouldn't decide. When the data's murky, human oversight is key. A model might not know a recent deployment changed everything, or that it's a holiday weekend with a skeleton crew. These are calls a machine just can't make.

What Needs to Change

For businesses, the path is clear yet challenging. Every AI action should be vetted against live system signals. If the resilience budget is tight, the agent pauses. No exceptions. This isn't just about tech sophistication, but about understanding that every AI action is a potential chaos event.

It's time for a reality check. Audit your AI agents, see where they fit within your resilience framework, and set boundaries. Don't let them act unchecked and risk becoming the invisible hand that tips your tech scales into chaos.

The companies that master this balance won't just avoid disaster. They'll be the ones leading the charge in reliable, scalable AI deployment. The question is, will your company be one of them, or will you wait for a crisis to find out?