The traditional view of AI collaboration assumes a simple model: agents work together toward a shared goal, following predetermined protocols and maintaining consistent roles throughout their interaction. This linear approach may have sufficed when AI systems operated in isolation or handled straightforward tasks, but it falls short in today's complex multi-agent environments.

The reality of modern agentic AI workflows is far more nuanced. Just as human organizations navigate partnerships that blend cooperation with healthy competition, AI agents increasingly need the flexibility to shift between collaborative and competitive modes depending on the task at hand.