What an environment does
A reinforcement learning environment gives an agent a task, tools, a world it can change, and feedback on the result. For engineering work, that means making the repository, execution conditions, and scoring behave like a coherent system.
My work
At Proximal Labs I built environments for frontier AI research, along with agents and techniques that generate those environments. My work also included investigating how coding agents plan, use tools, and fail on difficult tasks.
A public example: FrontierSWE
I contribute to FrontierSWE, a public benchmark for long-running engineering tasks. Its first release includes 17 tasks across implementation, performance, and research, with a 20-hour budget per task. The public announcement explains the benchmark design and results.
Why this is interesting
A useful task needs more than a plausible prompt. It needs a reproducible starting state, meaningful constraints, and a way to distinguish a correct result from an answer that merely looks finished. That combination makes environment engineering a systems problem.