The capability of frontier agents is increasingly determined less by the static text they read than by the environments they act in. But environments that are realistic, diverse, verifiable, and hard to exploit remain difficult to build at scale. This talk shares how Scale AI constructs agentic environments end to end and turns them into training and evaluation signal.

We begin with the raw material: collecting real-world artifacts—codebases, documents, web applications, enterprise workflows—and converting them into interactive tasks with grounded, checkable outcomes. We then show how these compose into a "universe" of environments spanning domains, tool surfaces, and difficulty levels, and describe the quality-control pipeline of expert review, automated verification, and adversarial probing that keeps tasks faithful and resistant to shortcuts. From the same infrastructure we derive benchmarks that measure what agents can actually do, and argue that training environments and benchmarks should be designed together but kept strictly separate.

On the training side, we cover how environment rollouts and expert trajectories feed SFT and RL post-training, and present practical techniques for mitigating reward hacking, including multi-signal and rubric-based rewards, hidden verifiers, held-out environment variants, and trajectory-level auditing. Finally, we turn to multi-agent systems and share methods for improving the efficiency of agent teams—task decomposition, communication protocols, and credit assignment—so that adding agents adds capability rather than cost.