RL-gyms for AI agents
Push your agent on context-rich simulated environments and specialized RL-gyms. Get high-fidelity trajectories and graded eval signals for training and evaluating AI agents at scale.
Harness-agnostic by design: use Toloka’s harness
or yours—with grading hooks and user-LLM emulation.
What we build
Model Context Protocol replicas of enterprise tools with realistic schemas, data flows, and permission models.
Isolated, containerized browsers and interactive web applications, instrumented for DOM/screen diffs and tool/API calls.
Multi-user virtual organizations with realistic communications, document exchanges, approvals, and business processes that produce stateful context over time.
Real expert teams executing authentic workflows with full artifact capture across version control, project management, and communication tools.
How it works: environments built by engineers, for engineers
Managed end-to-end environment and data operations
Requirements & scope
You give goals, constraints and success criteria. We translate them in environments, trajectory schemas, rubrics, and QA plans.
Environment design
Containerized testbeds with seeded data and instrumented trajectory capture, invariants, and event log.
Calibration and seed tasks
Domain experts execute seed tasks; we validate invariants, success metrics, and telemetry to stabilize the environment.
Data collection
You give goals, constraints and success criteria. We translate them in environments, trajectory schemas, rubrics, and QA plans.
Hybrid QA (AI Agent + human)
QA AI Agent verifies trubric adherence, logical consistency, environment invariants, task completion, and structural integrity. Senior QAs audit complex, flagged, or sampled cases. Feedback always tunes QA Agent
Delivery and integration
Receive versioned datasets, eval reports, and structured outputs ready for training and benchmarking. Always audit-ready.
Harness-agnostic adapters available; human, scripted, and LLM-judge grading are supported.
Instrumentation and logging
Complete trajectory capture with state-action sequences, tool/API interactions, timing signals, environment versions/seeds, and screen/DOM diffs.
Deterministic replay
Versioned environments, deterministic resets, and controlled seeds enable exact repro of agent runs and human trajectories.
Structured outputs
Per-step/per-task labels, failure categorization, safety flags, and calibrated scores for SFT and RLAIF workflows.
Where this applies
PII scrubbing and policy-compliant use of foundation models with client-approved data handling.
Secure, containerized execution
and controlled credentials
in isolated testbeds.Comprehensive audit logs covering environment versions, configs, reviewers, and QA outcomes for exact repro.
Frequently Asked Questions
Ready to accelerate agent development?
Bring us a target workflow, a tool stack, or a training/eval gap.
We’ll showcase the plan we’d run end-to-end to get you to your goal.