The advancement of AI models indicates a potential transition into an 'agent era,' where new frameworks and strategies developed to address previous weaknesses may finally empower agents to operate effectively. Despite the historical challenges faced by AI agents with open-ended tasks, improvements in model capabilities suggest that the integration of these frameworks could lead to a significant enhancement in performance. Notably, the new models excel in structured environments, particularly in complex tasks involving mathematics and coding, where capabilities such as search functions and chain of thought reinforcement learning demonstrate marked superiority over earlier generations.

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