OpenAI’s o1’s reasoning ability is essentially chain-of-thought behind the API, which makes it a much more powerful model for handling nuanced problems. Chain-of-thought invites the LLM to iteratively “think out loud” by revisiting its internal monologue until it has produced a satisfactory answer. We know that this produces superior results to one-shot responses, and here it’s been productized.
Agents are also characterized by iterative behavior. But there’s a key difference: while models like o1 iterate internally to refine their reasoning, agents engage in iterative interactions with the external world. They perceive the environment, take actions, observe the outcomes (or side effects) and adjust accordingly. This recursive process enables agents to handle tasks that require adaptability and responsiveness to real-world changes.
So o1 implements a behavior that formerly we would have used a simple agentic workflow to mimic, at greater expense and latency. This is good! But internal reasoning does not replace the outcome-driven behaviors that characterize the promise of AI agents.
In the limit, if o1 was itself an “agent” by any definition, capable of acting on its own, we would still want to formalize methods of deploying it against a specific objective in a repeatable, observable manner.
Does OpenAI's o1 mean agents are dead?
— Jeremiah Lowin (@jlowin) September 13, 2024
o1's reasoning ability is essentially chain-of-thought behind the API, which makes it a much more powerful model for handling nuanced problems. Chain-of-thought invites the LLM to iteratively "think out loud" by revisiting its internal…