Data Solutions

Platform

Resource Hub

Company

Arena

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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.

Talk to an expert

Trusted by Leading AI Teams

What we build

MCP replicas of enterprise tools

Model Context Protocol replicas of enterprise tools with realistic schemas, data flows, and permission models.

MCP replicas of enterprise tools

Model Context Protocol replicas of enterprise tools with realistic schemas, data flows, and permission models.

Computer-use mockups

Isolated, containerized browsers and interactive web applications, instrumented for DOM/screen diffs and tool/API calls.

Synthetic companies

Multi-user virtual organizations with realistic communications, document exchanges, approvals, and business processes that produce stateful context over time.

Human-simulated virtual companies

Real expert teams executing authentic workflows with full artifact capture across version control, project management, and communication tools.

Human-simulated virtual companies

Real expert teams executing authentic workflows with full artifact capture across version control, project management, and communication tools.

How it works

Managed end-to-end environment and data operations.
Built by engineers, for engineers.

Requirements
& scope

Requirements & scope

You share your goals, constraints and success criteria. We translate them in environments, trajectory schemas, rubrics, and QA plans.

Environment
design

Environment design

Containerized testbeds with seeded data and instrumented trajectory capture, invariants,
and event log.

Calibration
and seed tasks

Calibration and seed tasks

Domain experts execute seed tasks; we validate invariants, success metrics, and telemetry
to stabilize the environment.

Data
collection

Data collection

We run demonstrations, targeted eval tasks, and long-horizon workflows to generate trajectories and graded eval signals.

Hybrid QA
(AI Agent + human)

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.

Delivery
and integration

Delivery and integration

Receive versioned datasets, eval reports, and structured outputs ready for training and benchmarking. Always audit-ready.

Instrumentation and reproducibility

Instrumentation and logging

Complete trajectory capture with state-action sequences, tool/API interactions, timing signals, environment versions/seeds,  and screen/DOM diffs.

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.

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.

Structured outputs

Per-step/per-task labels, failure categorization, safety flags, and calibrated scores  for SFT and RLAIF workflows.

Where this applies

Web agents

Enterprise automation

Code agents

On-device and constrained agents

Safety-conscious workflows

Domain-specific agents
(Tau-style RL-gyms)

Multi‑step navigation, e‑commerce workflows, and form completion in realistic site contexts.

Aligns with public web‑interaction benchmarks (e.g., WebArena/VisualWebArena, Mind2Web, WebShop/MiniWoB++), while adding enterprise‑grade context and replayable traces.

Web agents

Multi‑step navigation, e‑commerce workflows, and form completion in realistic site contexts.

Aligns with public web‑interaction benchmarks
(e.g., WebArena/VisualWebArena, Mind2Web, WebShop/MiniWoB++), while adding enterprise‑grade context and replayable traces.

Enterprise automation

CRM updates, document processing, approvals, and cross‑tool workflows in MCP replicas.

Covers tool‑use and planning tasks similar in spirit to multi‑tool agent benchmarks; our replicas add real permission models and auditable telemetry.

Code agents

End‑to‑end SDLC tasks in human‑simulated virtual companies with full artifact capture.

Complementary to the SWE‑bench family (incl. Lite/+), adding multi‑actor context (tickets, reviews, CI) and long‑horizon workflows.

On-device and constrained agents

Lighter RL‑gyms and Tau-Bench/Tau2‑style micro‑tasks for resource‑limited settings; supports end‑state‑only checks when needed.

Safety-conscious workflows

Controlled sandboxes for policy adherence checks and red‑teamable flows; supports human, scripted, and LLM‑judge grading.

Domain-specific agents
(Tau-style RL-gyms)

For regulated or niche verticals (finance, healthcare, legal, and more), we offer a Tau-style RL-gym process designed to produce RL-useful datasets and discriminative evals:

Domain knowledge → Environment & grading engineering → Test-case design & calibration

Privacy, security, and reproducibility

PII scrubbing, policy-compliant use of foundation models,  and client-approved data handling.

Secure, containerized environments and controlled credentials in testbeds.

Versioned environments, deterministic resets, and audit  logs for exact repro.

Partner with Toloka

Why Data Partnership?

Technologies

Offload environment engineering, data collection, and QA operations  to a team that does this full-time.

Faster to first useful dataset; more flexible than hiring for bursty, specialized work.

Why Toloka

Diverse and scalable supply

Depth in agentic data: instrumented, stateful environments—not just annotation.

Hybrid QA that blends tool‑enabled checks with senior human judgment, tuned to your rubric.

A rigorously vetted expert network with measurable quality controls.

Audit-ready reproducibility: versioned environments,  deterministic resets, and comprehensive logs.

For Tau-style RL-gyms: calibrated difficulty targeting  ~50% pass rate and a dedicated tri-role expert pipeline.

Dive deeper

Diverse and scalable supply

FAQ

How realistic are the environments?

MCP and computer‑use mockups replicate real tool schemas, workflows, and permission models. Synthetic and human‑simulated companies provide multi‑user context and realistic artifacts over time.

Can we bring our own data, tools, or credentials?

Yes. We integrate with your tool stack and data under client‑approved handling policies. Credentials are stored and used in controlled, containerized testbeds.

How reproducible are runs?

Every run references a versioned environment with deterministic reset procedures and controlled seeds. Audit logs enable exact reproduction of agent and human trajectories.

Do you support custom workflows and edge cases?

Yes. We scope custom tasks, invariants, and success criteria during planning and extend environments as requirements evolve.

What about quality?

Hybrid QA combines automated verification by the QA Agent with senior human review. Metrics and thresholds are aligned to your rubric and updated between batches.

How quickly can you stand up a pilot?

Most pilots deploy in under a month; production scale depends on environment breadth and integrations.

How do you handle privacy and security?

PII scrubbing, policy‑compliant models, secure containers, controlled credentials, and comprehensive audit logging are standard.

How do costs scale?

Pricing accommodates project‑based and ongoing usage patterns. Volume discounts are available for larger, sustained runs.

Trusted by Leading AI Teams

Fuel your AI agents
with  expert-crafted data

Talk to an expert

RL-gyms for AI agents

What we build

MCP replicas of enterprise tools

MCP replicas of enterprise tools

Computer-use mockups

Synthetic companies

Human-simulated virtual companies

Human-simulated virtual companies

How it works

Requirements & scope

Requirements & scope

Environment design

Environment design

Calibrationand seed tasks

Calibration and seed tasks

Data collection

Data collection

Hybrid QA(AI Agent + human)

Hybrid QA (AI Agent + human)

Deliveryand integration

Delivery and integration

Instrumentation and reproducibility

Instrumentation and logging

Instrumentation and logging

Deterministic replay

Deterministic replay

Structured outputs

Structured outputs

Where this applies

Web agents

Enterprise automation

Code agents

On-device and constrained agents

Safety-conscious workflows

Domain-specific agents(Tau-style RL-gyms)

Web agents

Enterprise automation

Code agents

On-device and constrained agents

Safety-conscious workflows

Domain-specific agents(Tau-style RL-gyms)

Privacy, security, and reproducibility

Partner with Toloka

Why Data Partnership?

Technologies

Why Toloka

Diverse and scalable supply

Dive deeper

Diverse and scalable supply

FAQ

FAQ

Fuel your AI agents with expert-crafted data

Requirements
& scope

Environment
design

Calibration
and seed tasks

Data
collection

Hybrid QA
(AI Agent + human)

Delivery
and integration

Domain-specific agents
(Tau-style RL-gyms)

Domain-specific agents
(Tau-style RL-gyms)

Fuel your AI agents
with  expert-crafted data