Prax: Programmable Synthesis of Verifiable Environments for Working Agents

Training capable agents for real-world work requires large collections of diverse, executable, and verifiable environments, yet such environments remain expensive to author and difficult to synthesize reliably. Unlike text-only data, a work environment forms a self-contained system: task instructions, underlying state, tool interactions, and verification logic must remain mutually consistent. Directly generating these components with LLMs often produces environments that appear plausible but are actually unsolvable, inconsistent, or incorrectly scored. We introduce **Prax**, a framework for **programmable synthesis of verifiable work environments**. Prax represents an environment as a structured specification that separates invariant task logic from generative parameters, executable ground-truth derivations, rendering rules, and reference instances. This representation enables scaling along two complementary axes: **(i)instantiating the same task structure across new scenarios, entities, and states**, and **(ii)applying correctness-constrained transformations that systematically alter reasoning structure, difficulty, and failure modes.** To preserve verifiability, Prax generates environment facts before deriving expected outcomes, and validates each synthesized instance with programmatic checks for structural validity, tool-call closure, state–verifier consistency, answer leakage, and runtime compatibility. We further execute synthesized environments with agents and reject trajectories that fail execution or quality criteria before training. Starting from over 20k synthesized task intents, Prax constructs 25k reusable specifications and over 65k environment instances spanning diverse workflows. Training on the verified trajectories yields state-of-the-art performance on general knowledge tasks at 35B size, demonstrating that Prax can turn a small collection of expert-designed tasks into scalable, high-quality supervision for agents that perform real-world professional work.