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02 / Capability

Teach agents to finish real work.

TazQ provides agentic AI data: human-demonstrated trajectories of real tasks across tools, browsers and software, realistic environments with clear success criteria for reinforcement learning, and expert evaluation of what agents actually do. Teams use it to build agents that plan, use tools and recover from mistakes.

Plan an agentic AI project See how it works

Updated Trajectories · RL environments · Tool use · Agent evaluation

Definition

What is agentic AI data?

Agentic AI data is training and evaluation data for AI systems that take actions, not just answer questions. It records how a skilled person completes a multi-step task, every click, command, tool call and decision, and defines environments and success criteria so an agent can practise the same task and be judged on the outcome.

Why it matters

Agents learn from doing.

An agent is only as good as the examples and environments it learns from.

01

Real tasks have many steps.

A single mistake early in a long task can derail everything after it. Agents need examples that show complete, realistic sequences.

02

Success has to be checkable.

Reinforcement learning needs environments with clear goals and reliable checks, so an agent knows when it has truly finished.

03

Failures hide in the details.

Agents fail in ways that only show up when you watch them work. Expert review finds where they went wrong and why.

Free: the Agentic AI readiness checklist

Seven decisions to make before collecting data for an agent, so the data teaches it to finish real tasks safely.

How TazQ does it

From a real task to a capable agent.

Every agentic project follows four steps, shaped around the work your agent must do.

  1. 01

    Design realistic tasks.

    Choose tasks that reflect real work, with clear goals, starting states and the tools an agent is allowed to use.

  2. 02

    Demonstrate them, step by step.

    Skilled people complete each task while every action, observation and decision is recorded as a trajectory.

  3. 03

    Build environments and checks.

    Package tasks into reproducible environments with automatic and human success checks for training and testing.

  4. 04

    Evaluate what agents do.

    Experts review agent runs, label where and why they failed, and turn the findings into new tasks and data.

Deliverables

What you receive.

Formats adapt to your agent framework. These are the data types agent teams ask for most.

Agentic AI data types, what each includes, and typical formats
DataWhat it includesTypical formats
Human trajectoriesComplete demonstrations of multi-step tasks, with every action, observation and reasoning note.JSONL action logs, screenshots
Computer-use recordingsBrowser and desktop sessions with synchronised screens, clicks and keystrokes.Video with action and event logs
Task specificationsGoals, starting states, allowed tools and success criteria for each task.Task JSON or YAML
RL environmentsReproducible environments with verifiable checks an agent can practise in.Packaged environment with checker
Agent evaluationsExpert-scored agent runs with labelled failure points and their causes.Scored runs with failure labels
Failure taxonomiesRecurring ways your agent fails, grouped and ranked to guide the next round of data.Report with examples

Want to see one?

Request an anonymised sample of any deliverable. A specialist will share a relevant example where available.

Use cases

Where teams use it.

01Computer-use and browser agents

Demonstrations and environments for agents that navigate websites, fill forms and operate desktop software.

02Coding agents

Real engineering tasks, from reading a codebase to fixing a bug and passing tests, with expert review of each change.

03Tool-using assistants

Trajectories that show when to call which tool, how to read its result and how to recover when it fails.

04Enterprise workflows

Multi-step processes in domains like finance, operations and support, captured by people who do that work.

Quality & experts

Quality starts with people.

Practitioners, prepared

Contributors who do the work in real life are prepared through TazQ Academy before they demonstrate or evaluate tasks.

Explore TazQ Academy

Reproducible by design

Tasks and environments are versioned and repeatable, so results can be compared across agent versions.

Verified outcomes

Success is checked automatically where possible and by experts where it takes judgment, with disagreements reviewed.

FAQ

Questions, answered.

01

What is an agent trajectory?

A trajectory is the full record of how a task was completed: each step’s observation, the action taken, and often the reasoning behind it. Human trajectories show an agent what competent, efficient work looks like from start to finish.

02

What is an RL environment for AI agents?

It is a reproducible setting, such as a website, app or sandboxed computer, with a defined task and a way to check success. Agents practise in it and are rewarded for reaching the goal, which lets them improve through reinforcement learning.

03

Can you record computer-use and browser tasks?

Yes. Contributors complete tasks in browsers and desktop applications while screens and actions are recorded and synchronised, ready for training or evaluation.

04

How do you evaluate AI agents?

Agents run on held-out tasks, their outcomes are checked automatically where possible, and experts review the runs to judge efficiency, safety and correctness, labelling exactly where things went wrong.

05

Which domains can tasks cover?

Tasks can be designed for general computer use or for specific professional domains, with contributors chosen for real experience in that work.

06

How do we start an agentic AI project?

Share the kind of tasks your agent should handle, its tools and your framework. A pilot set of tasks and trajectories then validates the format and quality bar before scaling. You can outline this in the project brief below.

Related capabilities

All capabilities
01 /↗

Frontier Alignment

Expert demonstrations, reasoning traces, and human preferences that help capable models become more useful, reliable, and aligned.

  • RLHF
  • Reasoning
  • SFT
05 /↗

Model Integrity

Human evaluation that investigates hallucinations, bias, and failure modes—turning uncertain model behavior into clearer quality signals.

  • Evaluation
  • Safety
  • Robustness

Start your Agentic AI project

Let’s build
what comes next.

A better model starts with a clearer question.
What do you want yours to understand?

For AI teams

Scope your project in about a minute. A specialist reads every brief and replies with next steps.

For human expertsExplore TazQ Academy
YOUR PROJECT, IN FOCUS
Who are you?
What are you building?

Choose all that apply.

What brings you here now?

Choose up to two.

Where are you today?

Stage

When would you like to start?

Scale (optional)

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Languages or domains (optional)

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Your role (optional)

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