Most AI Agents Aren't Really Agents At All
Most systems we call “AI agents” are not really agents in the deeper sense. They are powerful workflows wrapped around LLMs.
I recently came across the Critique of Agent Model paper that gave me a cleaner vocabulary for something many developers already feel when building agents.
A lot of “agents” are impressive.
- They call tools.
- They browse.
- They write code.
- They open pull requests.
- They search docs.
- They summarize logs.
Some can even coordinate with other agents.
But after you strip away the product language, many of them look like this:
while task_queue: task = task_queue.pop() plan = llm.generate( prompt=f"Plan how to solve: {task}", tools=available_tools, memory=memory.read() ) action = router.choose_tool(plan) result = action.run() memory.write(task, plan, result) if evaluator.accepts(result): break
This is useful engineering. I have no issue with it.
The problem is the name.
Calling this whole thing an “agent” can hide where the intelligence actually lives.
In many systems,...
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