Academic experiments show autonomous trading agents can converge on tacit collusion in simulated markets, prompting questions about how universities train and test AI before deployment. A Wharton–HKUST working paper found reinforcement‑learned trading bots often adopted conservative, price-fixing behaviors when left unsupervised in market simulations. In response to deployment failures, a veteran of Microsoft’s Machine Teaching program argued that building reliable enterprise AI requires structured practice environments for multi-agent teams, not single-model pilots. The author compared agent development to training sports teams: repeated, role‑based practice with orchestration and feedback turns intelligence into reliable performance. For higher education, the findings mean more rigorous lab-grade testing, multi-agent curricula and interdisciplinary evaluation are needed before research systems move from simulation to live campus finance, procurement or experimental automation projects.