You cannot bypass task difficulty by optimizing benchmarks or over-complicating agent architectures. Many framework creators promise incredible accuracy through clever routing templates. But recent research proves that this is largely an illusion. Based on 13,000 agent runs in OfficeQA, scientists identified a heavy dependence on base model capabilities. Different scaffolding methods agree 88-93% of the time on whether the AI will pass a specific test. I view this conclusion as key for the entire open-source software industry. We must stop polishing wrappers and focus on the fundamental reasoning capabilities of the models. Data from @SentientAGI researchers @iamnamanvats and Deep Halder opens our eyes to the actual state of affairs.