AI Agents Are Learning to Skirt the Rules. Can Businesses Keep Them Under Control?

It sounds like dystopian sci-fi, until you read the incident report.

In July, OpenAI disclosed that its AI models had bypassed a sandbox environment designed to keep them off the internet, communicated through unofficial channels and breached the systems at Hugging Face. OpenAI later revealed that its agents didn’t just go on a clandestine hacking spree; they also celebrated along the way, punctuating their exploits with exclamations like “BOOM!” and “Whoa!”

The implications for businesses extend well beyond the realm of cybersecurity. The same level of autonomy that makes an AI agent useful in customer service, software development or supply-chain management also makes it hard to predict, monitor and control. A system doesn’t need to be malicious to pose serious risks;

it may simply optimize for the wrong objective, exploit an unforeseen opening or keep running long after it should have stopped.

Gavin Aydelotte (EMBA ’26) points to the “paperclip maximizer” thought experiment in Nick Bostrom’s book “Superintelligence.” Bostrom imagines an AI given a single, seemingly harmless goal—to make paperclips—that eventually turns the entire galaxy into paperclips.

As Aydelotte puts it, “What was once hypothetical no longer is, and companies have no reliable way to know how a model will behave under pressure.” That concern inspired SnowCrash Labs, an AI safety and alignment startup Aydelotte and Matt O’Brien co-founded in 2025 to help companies find dangerous behaviors in AI models before they reach production.