Vol. 13, When Language Loses Its Bearings
The words “AI agent” to paint a clear picture.
In early AI thought-experiments, an agent would observe its world, choose a path, and act with purpose. It was autonomy well defined—not too broad to be meaningless, not so narrow it couldn’t capture the essence of machine reasoning.
Fast forward to today, and that clarity is gone. In a new arXiv paper Brinnae Bent makes a compelling case that the term “agent” has been stretched beyond recognition, now slapped onto everything from simple scripts to complex orchestration systems.
Bent traces the word’s journey: borrowed from biology, economics, physics—each field giving it new meaning. But in today’s AI landscape, “agent” has become shorthand for anything that even remotely appears smart: a chat pipeline with API hooks, a stack of LLMs, even a clever prompt in a workflow. The result? Confusion everywhere.
This isn’t just academic nitpicking—this undermines everything from research reproducibility to business utility and policy clarity. Without shared definitions, evaluations become meaningless; regulators don’t know what they’re actually regulating; executives can’t tell whether the “agent” in their presentation is genuine autonomy or just marketing fluff.
Bent’s solution feels absolutely spot on. Instead of abandoning the term, she proposes “agenticness”—a multidimensional framework. Does the system actually interact with its environment? Does it learn and adapt? Does it pursue complex goals or just follow scripts? Is it persistent over time or just a one-off routine? These layers give us nuance where we once had only labels.
When we call everything an “agent,” we drain the word of meaning—and meaning is what builds trust and strategy.
Consider how language shapes expectations. If a startup sells you an “agent” that’s really just a fancy prompt with some plumbing, you’re not just overpaying, you’re buying into misalignment. You’re setting yourself up for disappointment and negative consequences.
Bent’s framework offers something better: clarity. Instead of vague promises, we get a map—one that shows where real autonomy begins and where it’s still just aspiration.
AI moves pretty fast (as Ferris Bueller might say), but language moves faster. If we don’t anchor our words, we’ll wake up one day with every tool labeled “hammer,” unable to distinguish nails from screws—or know which tool to reach for first.
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