Why AI agents might not be right for you

There’s no question that 2026 is the year of the artificial intelligence agent. AI agents are remarkably ubiquitous, as people use them to write code, communicate with customers, identify and resolve issues in their information technology environments, and execute complex tasks within enterprise environments.

It seems there’s nothing that agents cannot do. And yet, the risks inherent in agentic behavior are in the news almost as much as their success stories. Horror stories of agents going off the rails, breaking out of sandboxes and hacking other companies and government agencies fill the airwaves.

Your organization’s AI strategy may depend on AI agents, but given the risks, should you really be depending on them for your company’s success?

Nondeterministic behavior: Agents’ greatest strength and greatest weakness

Any decision to deploy agents depends upon understanding their nondeterministic behavior.

Today’s agents generally depend upon large language models. LLMs take human language inputs and produce human language outputs by predicting the next word in a sequence.

Given the way the models are trained, repeating a given input may produce quite different outputs. In other words, LLM-based agents are inherently nondeterministic.