Keep it simple, stupid: Agentic AI tools choke on complexity

Agents may be the next big thing in AI, but they have limits beyond which they will make mistakes, so exercise extreme caution, a recent research paper says.

According to a definition by IBM, agentic AI consists of software agents that mimic human decision-making to solve problems in real time, and this builds on generative AI techniques by using large language models (LLMs) to function in dynamic environments.

But while the industry hype machine pushes agentic AI as the next big thing, potential adopters should be wary, as the paper, "Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models" [PDF] argues that LLMs are incapable of carrying out computational and agentic tasks beyond a certain complexity level, above which they will deliver incorrect responses.

The paper uses mathematical reasoning to show that if a prompt to an LLM specifies a computational task whose complexity is higher than that of ...