Reasons inference scaling models like OpenAI o1 might not benefit Agents

I was surprised when I read that OpenAI o1 model does not seem to benefit agents. If a model can think and reason better than it should help agents make better decisions and be more reliable. In the CORE-Bench benchmark o1-mini scored 24% compared to 38% with Claude 3.5 Sonnet. The article cites two main reasons:

• Inference scale models like o1 require different prompting styles than regular models, and current agentic systems are optimized for prompting regular models.
• Reasoning or inference scale models so far have not been trained using reinforcement learning in a setting where they receive feedback from the environment — be it code execution, shell interaction, or web search. In other words, their tool use ability is no better than the underlying model before learning to reason.

Another reason I read is that o1 can sometimes behave strangely when integrated into custom agentic architectures, since it was trained to follow a specific reasoning process—indeed, OpenAI actively discourages users from using prompts that ask the model to reason in any specific way.