Since the "DeepSeek moment," the frontier labs have quietly shifted how they train agents — away from human feedback and toward RL environments with automated, verifiable rewards. The same pattern is now reaching application teams, and most of us haven't caught up.
This talk is a hands-on introduction to RL environments for LLM agents. We'll cover what they are, why they make eval suites look primitive by comparison, and the core components you need to build one. Then we'll build a working environment for a multi-step agent — verifiable rewards, real episodes, and a feedback loop that lets the agent improve itself between runs without any fine-tuning.
The same 200 lines that grade your agent can also teach it.
Shiva is a Founding ML Research Engineer at Brevian, where he builds the agent and evaluation infrastructure behind a sales intelligence platform that turns enterprise knowledge into real-time guidance for revenue teams. His work spans agentic systems, RL environments, and the layer that bridges applied LLM research with reliable production deployments.
