The increasing ease of deploying AI agents in software development is not translating into higher productivity, as developers face the "orchestration tax"—the hidden costs of managing, evaluating, and integrating agent outputs. While launching agents is simple, the real challenge lies in the human judgment required to verify results, resolve conflicts, and make architectural decisions, which cannot be parallelized. Developers are likened to the Global Interpreter Lock (GIL) in Python, a single-threaded resource that limits throughput in concurrent systems. Despite multiple agents running simultaneously, the bottleneck remains the developer's cognitive bandwidth, leading to longer queues of tasks and potential cognitive fatigue. Effective workflows must focus on designing attention architectures, balancing machine delegation with human oversight to prevent technical and cognitive debt.