Agentic AI and the Mythical Agent-Month (muratbuffalo.blogspot.com)

A recent position paper explores the concept of "Scalable Agency" within Agentic AI, suggesting that AI agents could significantly reduce the time required for software integration by operating in parallel without the ramp-up delays that human engineers face. The authors propose a paradigm where future infrastructure could autonomously design and evolve itself, termed Self-Defining Systems (SDS). However, the paper ultimately falls short of substantiating its ambitious claims, especially regarding Time to Integrate (TTI) and fails to address foundational concepts clearly. Critics argue that the core assumption of SDS—that software engineering tasks can be executed in parallel—is flawed, as the paper's own case studies highlight the coordination complexities agents encounter. For instance, while agents could rapidly produce a monolithic LLM runtime, they faltered in generating innovative designs and took significantly longer to integrate on existing systems, revealing that communication and shared understanding remain crucial in complex system architecture. This highlights that, contrary to the promise of bypassing Brooks' Law, multi-agent systems face similar coordination challenges, potentially making integration more complex and less efficient than anticipated.