Talk to an Expert
Talk to an Expert

RL environments for enterprise workflows

Where agents learn the job

Built for frontier agents.

Expert-built replicas of the enterprise apps companies run on, with all the friction of a busy workweek. Train agents on the work, then measure whether they can actually do it.

Scope your environment See how it works

Agents that chat. But can they work?

An RL environment is a realistic simulation of enterprise systems, where agents practice multi-step work at scale without touching production data.

Train

Train agents on the job

Run agents through enterprise workflows at scale, with a stable reward signal.

Evaluate

Measure whether they can do it

Score multi-step work against the final system state.

Stateful simulation

Every action changes the world.

Dense reward signal

Feedback at every step, not just pass/fail.

Deterministic reset

Same starting point, every run.

The vision

Real environments. Real expertise. Real obstacles. Real economic value.

Real environments

Working apps, not mock-ups. Each runs its own server and database, with screens for people and APIs for agents, and together they share one company: the same people, calendar and week.

Real expertise

Every company, record and rule inside is authored by domain experts. When the world is right, the reward is right, so agents learn from signals you can trust.

Real obstacles

The company keeps moving while the agent works.

  • A rule changes mid-week
  • A teammate is out
  • A lookalike sender
  • Instructions buried in a message
  • A date that reads two ways
  • A deadline that moves up
  • People who reply late

Real economic value

Every task is a stretch of a paid role, judged on the outcome a business needs. Frontier models fail most of the hardest ones, and every task comes with a pass@k difficulty analysis.

Across domains

Environments span the software companies run on, all built to the same standard.

  • Productivity suite
  • Team messaging
  • Recruiting and HR
  • E-commerce
  • Customer service
  • Travel booking
  • CRM
  • Custom domains

The proof

API and GUI. One world.

Every environment exposes the same app through an API, MCP and the screen, on one backend. Run the same work both ways on two copies of the world and they come out identical, checked after every step.

  • Computer use · GUI

    Agents work the screen with clicks and keystrokes, like a person.

  • Tool use · API and MCP

    Agents call the same apps directly through APIs and MCP servers.

  • One backend

    API, MCP and GUI reach the same app and the same data.

  • Same state, every step

    An action taken by call or by screen leaves the world in the same state.

  • Use either path

    Train and evaluate through the interface your agent actually uses.

Cross-application workflows

Work never stays in one app.

The hardest tasks span many tools. Agents have to switch apps and carry context from one to the next.

Why teams choose Abaka

Aligned, expert-built, hard by design, and yours.

01 · Aligned

One world, every way in

  • API, MCP and GUI on one backend
  • Same state after every step
  • Any interface your agent uses

02 · Expert-built

Ready to train

  • Domain experts author every record and rule
  • Golden trajectories for every task
  • Verifiers: state checks plus rubrics

03 · Hard by design

Hard enough to matter

  • Frontier models fail most of the hardest tasks
  • pass@k analysis on every task
  • Built-in obstacles, like rules that change

04 · Custom

Built to your spec

  • Your domains, or ours
  • Your workflows and difficulty targets
  • Delivered as containers for your stack

Agents don't fail at clicking. They fail at the job. Train them on the job.

The environment layer is no longer optional. It is foundational.

Scope your environment