Mercor’s Brendan Foody explains how RL environments teach AI agents real-world work

In a Sequoia Capital talk, Mercor co-founder Brendan Foody explains reinforcement-learning environments as a combination of realistic worlds, software applications, and tasks with verifiers. His central argument is that agents learn useful professional work by practicing inside high-fidelity simulations built with expert judgment, not from isolated prompts alone. He walks through Mercor’s APEX-Agents examples, how data quality depends on realism and accurate grading, and why human experts still matter even when models generate trajectories and help populate environments. The practical implication is that application companies may increasingly build proprietary training data and evaluations around their own workflows, with longer-horizon tasks and virtual coworkers as the next frontier.