Modern AI is shifting from single models to modular systems that plan first, then execute step-by-step. When something fails, they backtrack, diagnose the problem, and revise the plan. This approach handles complex tasks like web automation and coding better than older end-to-end models.
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I do see right now there is a shift from sort of these monolithic systems. So because when you, when you're solving these more complex tasks, whether it's a task that you're doing on the web, whether it's a task controlling your computer, whether it's a reasoning task, solving a math problem, especially for these agentic systems and coding problems as well. For these agentic systems, a lot of times the agentic systems would come up with a plan, with a high-level plan of how you would execute, and it would go and proceed in executing that in steps. And whenever you're failing in executing, you know, certain steps, you sort of roll back and try to fix mistakes. And so these systems becoming, you know, the way the reasoning And intelligence comes in is this notion of like creating the plan of what you need to accomplish.