The rapid advancement of Large Language Models (LLMs) has catalyzed the development of Multi-Agent Systems (MAS) for complex problem-solving. However, conventional MAS architectures often suffer from context loss and identity fragmentation during agent handoffs, where the transfer of state between specialized agents degrades reasoning fidelity. We present cursor-agent-team, a framework that reimagines human-AI collaboration through a multi-role, single-conversation architecture. By eliminating distinct agent instances in favor of role-switching within a unified context window, we aim to reduce handoff overhead—a design goal we term Minimal Handoff. Our methodology integrates Aspect-Oriented Programming (AOP) principles to manage cross-cutting concerns—such as persona consistency and knowledge gleaning—without polluting the core logic. We also employ a Spec-Script loop where natural language specifications drive deterministic Python script execution, ensuring reliability. Implemented within the Cursor IDE, this framework illustrates how Intelligence Augmentation (IA) can be realized not by replacing humans, but by orchestrating AI capabilities as a cohesive team within a single interaction stream.
Kuang Hu (Wed,) studied this question.