An impressive AI demonstration can grab your attention. A realistic test asks a tougher question: what happens when the easy approach fails?
That’s the idea behind zSecurity’s experiment comparing OpenClaw, Hermes Agent, and Agent Zero against four targets with different levels of difficulty. The hardest challenge required linking multiple security weaknesses together to achieve remote code execution—making a target computer run commands remotely.
According to the creator, the agents approached the challenges differently. Some emphasized speed and familiar patterns. Others adjusted their approach when an initial attempt failed.
That distinction matters. Finding an obvious weakness is one thing. Working through several connected problems requires a different level of problem-solving.
For anyone evaluating AI tools, this experiment raises useful questions. Does the agent finish the task? Can someone verify its findings? How much human help does it need? What happens when it gets stuck?
Those questions also apply beyond cybersecurity. An agent that creates content, manages files, or helps maintain a website should be judged by dependable results.
Four targets offer a useful comparison, but they cannot establish which framework will perform best in every situation. The underlying AI model, available tools, and setup also deserve attention.
The practical lesson: look beyond the polished demonstration. Ask for clear goals, observable results, and an honest account of failures.
Would you trust an AI agent to work independently, or would you review every action first?
