The paper introduces the concept of "Scalable Agency," suggesting that AI agents could overcome Brooks' Law, which states that adding people to a late project delays it further. The authors claim that AI agents can quickly assimilate context, allowing thousands to work simultaneously on design tasks. They envision a future where infrastructure can self-design and evolve without human intervention, dubbed Self-Defining Systems (SDS). However, the article expresses skepticism about these claims, pointing out that the paper fails to substantiate its bold assertions, particularly regarding the reduction of Time to Integrate (TTI). Key terms like "specification" remain undefined, leaving fundamental concepts vague. The results cited in the paper raise further doubts about the effectiveness of AI agents. In a case study, agents built a monolithic LLM inference runtime that performed worse than a human-written counterpart. While agents were able to replicate existing techniques, they did not produce innovative solutions. When tasked with integrating a system on top of existing components, the agents took 35 days, facing issues attributed to various technical failures. The real challenge appeared to stem from the inherent complexity of distributed architectures rather than the agents' capabilities. The article highlights that multi-agent systems do not escape Brooks' Law; they merely encounter the challenges of coordination and understanding more quickly than humans. The paper's proposed progression from Self-Configuring to Self-Managing systems ultimately reveals that core responsibilities still lie with humans, negating claims of significant advancement in design capabilities. The comparison to the Shell Game podcast illustrates a practical failure of AI agents when placed in real-world scenarios. Despite ambitious goals, the experiment of running a startup with AI agents collapsed, highlighting that, without human insight and intuition, the promise of AI-driven self-management remains largely unfulfilled.