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ARCHYTASAI

03 · Environments

Environments. Where agents practice the whole job, and get checked on it.

Stateful worlds with real tools, policies and data, rewards that hold up under testing, and the expert data around them. Built for post-training and held-out evaluation.

Discuss a custom environmentBrowse the catalog

What we build. Data, tasks, environments and the rewards that make them trainable.

Each engagement combines these pieces for a specific capability, from expert data alone to full executable environments with tested reward functions.

  • FIG.3aData

    01Expert training data

    Tasks, demonstrations, preferences, reference answers and grading rubrics written by domain experts and grounded in real professional workflows.

    1. 1.1Demonstrations and reference answers
    2. 1.2Preference data
    3. 1.3Weighted grading rubrics
  • FIG.3bTasks

    02RL-verifiable tasks

    Hard reasoning and tool-use problems with reliable feedback, expert validation and evaluation criteria designed for post-training.

    1. 2.1Programmatic verifiers
    2. 2.2Reference solutions
    3. 2.3Difficulty tiers
  • FIG.3cEnvironments

    03Executable RL environments

    Stateful simulations with realistic tools, policies, data, resets and rewards, for training and evaluating agents across complete workflows.

    1. 3.1Tools and APIs
    2. 3.2Seeded initial state and resets
    3. 3.3Reward functions
  • FIG.3dEvals

    04Evaluations and reward engineering

    Custom benchmarks, programmatic verifiers, reward-hacking tests, failure analysis and held-out evaluations that show whether capability improved.

    1. 4.1Held-out evaluation suites
    2. 4.2Exploit and reward-hacking probes
    3. 4.3Failure analysis

Verticals. Environments by vertical, each built on a shared simulated world.

Within a vertical, every environment runs on the same simulated organization, so tasks can span systems and teams the way real work does. Each environment is built to order and adapted to the workflows you need to train or evaluate.

Ways to buy. From a pack of sample tasks to an audit of the environments you already run.

Pick the option that matches where you are. Every option comes with the same delivery metadata, so you know what you’re getting before training starts.

  • Option A

    ATask and dataset pack

    Teams that need curated training or evaluation examples without a full environment.

    Deliverables

    • Validated tasks
    • Data and annotations
    • Rubrics
    • Reference solutions
    • Optional SFT trajectories

    Commercial unit

    Accepted task instance, approved dataset batch or licensed task package

    Scoped and quoted per engagement.

    Request sample tasks
  • Option B

    BExecutable RL environment

    Model labs and AI-native companies running post-training.

    Deliverables

    • Environment runtime
    • Tasks and scenario generation
    • Tool interface
    • Verifiers
    • Reference trajectories
    • Integration documentation

    Commercial unit

    Environment development engagement, accepted task inventory and licensing rights

    Scoped and quoted per engagement.

    Request technical integration details
  • Option C

    CCustom training environment

    Companies that need tasks built on their own workflows and data.

    Deliverables

    • Workflow analysis
    • Domain model
    • Customer-specific scenarios
    • Expert annotations
    • Custom verifiers
    • Validated environment

    Commercial unit

    Scoped development project, with optional maintenance and expansion

    Scoped and quoted per engagement.

    Discuss custom development
  • Option D

    DEnvironment verification and hardening

    Teams already running RL environments.

    Deliverables

    • Grader audit
    • False-failure and test-soundness audit
    • Exploit tests
    • Solvability analysis
    • Difficulty calibration
    • Reproducibility review

    Commercial unit

    Audit or validation engagement

    Scoped and quoted per engagement.

    Discuss an audit

Every dataset and environment ships with

Generated variants count as accepted instances only if they pass the quality and novelty criteria agreed for the engagement.

  1. M.1Product identifier and semantic version
  2. M.2Domain and target competency
  3. M.3Data origin and licensing rights
  4. M.4Available instance count
  5. M.5Task distribution and difficulty calibration
  6. M.6Supported training and evaluation modes
  7. M.7Input and output schemas
  8. M.8Ground-truth construction method
  9. M.9Verification methodology
  10. M.10Train and evaluation split strategy
  11. M.11Acceptance testing and quality metrics
  12. M.12Permitted uses, exclusivity and redistribution terms

Every environment is built to order. Tell us which workflow you need to train or evaluate, and where you are in the process.

Request sample tasksDiscuss custom developmentRequest technical integration details