Post by Turing

Building production-ready RL environments for commercial workflows is not a UI problem. It is a systems problem. Turing delivered a fully operational RL Gym designed to train and evaluate AI agents on real-world sales execution across enterprise tools. Key build metrics: -100+ structured workflows spanning inbound and outbound sales motions -4 enterprise platforms replicated: LinkedIn Sales Navigator, HubSpot, Outreach, Calendly -50+ multi-platform workflows requiring coordinated execution across all four tools -Full coverage across integration tiers from single-platform to four-platform tasks -Pass@3 framework applied to calibrate workflow difficulty and generate reliable RL signals What makes this different from typical agent training setups: -Sandboxed UI replicas with realistic data and full state coverage -Natural-language prompts mapped to structured workflow blueprints -Step-level verifiers using assertion-based validation instead of heuristic scoring -Cross-platform evaluation using shared run IDs for end-to-end task validation -Standardized verifier API producing structured reward signals -Dockerized delivery for immediate integration into training pipelines This enables: -Objective measurement of agent performance at both step and workflow levels -Identification of failure modes within complex multi-step execution -Safe training on production-like systems without exposing live environments -Scalable experimentation across tools, workflows, and difficulty tiers Moving from isolated UI tasks to coordinated, multi-platform agent behavior requires infrastructure that mirrors real execution environments. This RL Gym is that foundation: https://lnkd.in/gvrE3b46