Hamburger_menu.svg

Expert RL Environments Built for Frontier Standards

Controlled reinforcement learning environments for training and evaluating agents. Start with scoped experiments to validate fit before scaling across custom or pre-built RL environments.

Request RL Environments

Advancing Agent Performance Through Reproducible Environments

Turing’s RL environments provide structured, reproducible UI and non-UI environments where agents can be evaluated, trained, and iterated against real-world workflows. Each environment includes prompts, verifiers, and seed data—packaged for controlled experimentation and reproducible research.

Structured RL Environment Capabilities

Each capability is available as a scoped environment. Experiments are designed to validate scope and performance before larger-scale integration.

UI Clones

Interactive replicas of enterprise and consumer apps such as Jira, Salesforce, and Zendesk. Workflows are defined with domain experts to ensure realism, and per-task verifiers confirm completion against golden states. These environments capture issue creation, sprint planning, and other critical flows for computer-use agents.

Backend Gyms

MCP-based environments that expose APIs and tool calls for function-calling agents. Policies, database schema, and realistic seed data are created with SMEs, providing authentic records. They are used for evaluating and training agent behavior at scale.

Trajectory Generation

Controlled RL Environment runs produce gold-standard trajectories for supervised fine-tuning. Tasks are structured for reuse across prompts and can be replayed for consistency. Each dataset supports curriculum progression from simple to complex scenarios.

Reward Model Training

Environments generate labeled trajectories that strengthen reward functions for RLHF. Verifier-driven reward and penalty signals provide the basis for reliable outcomes. This setup accelerates robust reward model development by ensuring every label has consistent QA.

Observability & Analytics

Harnesses replay scenarios and validate outcomes across prompts, models, and versions. Labs receive evaluation reports with pass/fail metrics and trajectory traces. This enables consistent A/B comparisons and tracking of agent performance over time.

Custom Gyms

Bespoke environments support multi-tool workflows across role and function. Each is packaged for reproducibility and structured testing, then delivered with SOPs, guardrails, and escalation paths aligned to policy. These environments replicate client-specific contexts while maintaining research-grade standards.

Scale and flexibility

Turing RL Environments are designed to match the scope of both enterprise and research demands.

1000+

environments across enterprise and consumer applications, both UI and non-UI.

Custom

multi-tool workflows supporting any role–function combination in enterprise contexts.

Designed for continuous improvement

Turing RL Environments are full loops from evaluation to iteration, not static testbeds.

Observability and analytics

to track performance across agent versions.

Closed-loop data

for supervised fine-tuning and reinforcement learning.

Expert prompts and verifiers

created by domain specialists for reproducibility.

Evaluation reports

with pass/fail results and reproducible scenario replays

Standards trusted by frontier AI labs

Accelerate agent performance with RL Environments

R&D-driven standards

Criteria and taxonomies aligned with research use

Transparent, auditable pipelines

Trace every trajectory and evaluation run end-to-end

Elite, domain-specific talent

PhDs, Olympiad-level specialists, and vetted SMEs

Human-in-the-loop + AI feedback loops

Combined review to catch edge cases and ensure reproducibility

Domain-expert collaboration

Policies, database schema, and realistic seed data records built with SMEs

Application-level specificity

Workflows designed for real tools (e.g., Jira: issue creation, sprint planning, backlog grooming)

Accelerate agent performance with RL Environments

Get your own RL Environment and run agents in reproducible, high-fidelity environments tailored to your workflows.

Request RL Environments