Building AI agents:
Data for training and evaluation
Enhance AI agent performance and safety with high-quality curated data for training and evaluation
Our data solutions
MCP mockups
Computer‑use mockups
Synthetic companies
Human-simulated virtual companies
MCP-bench extensions
TAU-bench extensions
TinyTAU for on‑device agents
Computer-use agent injection vulnerability red-teaming
Coding agent safety evaluation
MCP injection vulnerability assessment
Trajectories evaluation
Trajectories demonstrations
Our expert knowledge drives your agent innovation
Domains
Mathematics
Computer Science
Medicine
Psychology
Physics
Chemistry
Biology
Astronomy
Biotechnology
Bioinformatics
Law
Finance
Accounting
Economics
Teaching
Linguistics
Civil Engineering
Automotive Engineering
Religion
Language Arts
Philosophy
History
Performing Arts
Visual Arts
Mathematics
Computer Science
Medicine
Psychology
Physics
Chemistry
Biology
Astronomy
Biotechnology
Bioinformatics
Law
Finance
Accounting
Economics
Teaching
Linguistics
Civil Engineering
Automotive Engineering
Religion
Language Arts
Philosophy
History
Performing Arts
Visual Arts
Languages
English
French
German
Spanish
Hindi
Malay
Russian
Bengali
Filipino
Ukrainian
Vietnamese
Japanese
Tamil
Thai
Dutch
Korean
Arabic
Swedish
Turkish
Polish
English
French
German
Spanish
Hindi
Malay
Russian
Bengali
Filipino
Ukrainian
Vietnamese
Japanese
Tamil
Thai
Dutch
Korean
Arabic
Swedish
Turkish
Polish
English
French
German
Spanish
Hindi
Malay
Russian
Bengali
Filipino
Ukrainian
Vietnamese
Japanese
Tamil
Thai
Dutch
Korean
Arabic
Swedish
Turkish
Polish
English
French
German
Spanish
Hindi
Malay
Russian
Bengali
Filipino
Ukrainian
Vietnamese
Japanese
Tamil
Thai
Dutch
Korean
Arabic
Swedish
Turkish
Polish
Case Studies
Auto-verifiable tasks for Deep Research Agent
Our team has built a dataset to enhance the Deep Research Agent. Each task includes a complex domain-specific prompt and a set of rubrics for automatic answer verification. The agent’s performance on extensive online research tasks was significantly improved through end-to-end RL using this data.
View case details
Client type:
Leading AI Company
Experts:
MS & PhD in Finance
Accounting
Economics
Medicine
Linguistics
Education
Language:
English
Volume:
Application:
Enhancing Deep Research Agent using end-to-end RL
Trajectories annotation for Coding Agent
Our team annotated 5000 coding agent trajectories, evaluating every step of user interaction. The signal provided by this curated data helped enhance reasoning and agentic capabilities on the client's side.
View case details
Client type:
Coding AI agents startup
Experts:
Software architects
DevOps engineers
Backend engineers
Language:
English
Volume:
5,000 trajectories
500 per week
Application:
Coding agent for repository maintenance and bug-fixing tasks
Learn more about Toloka
Frequently Asked Questions
Fuel your AI agents with
expert-crafted data