RL Environments for training & benchmarking AI agents.

We build simulated . Your agent practices on them, gets scored, and improves over time.

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Why Theta

Built for real-world agent training.

Scores what actually matters

We measure real task completion, not surface metrics. Did the refund go through? Was the ticket resolved correctly? Your agent gets credit for outcomes, not guesses.

Trains on the edge cases

Edge cases break agents in production. Our environments inject failures, timeouts, and messy data your agent will actually face — so it learns to handle them.

Built for your workflow

Not a generic sandbox. We replicate the exact software your agent operates in — same forms, same states, same quirks. Train on what you'll deploy to.

SDK

Start training in minutes.

train.py
import thetabench

env = thetabench.make("shopify-admin", task_id="prod-001")
obs, info = env.reset()

# gymnasium-compatible

Works with Stable Baselines, RLlib, and anything that speaks Gymnasium.

Blog(6)

Why RL Environments Matter More Than You Think

Why RL Environments Matter More Than You Think

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Agent Companies Should Stop Building Training Infrastructure

Agent Companies Should Stop Building Training Infrastructure

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The Gap Between Demo and Production for AI Agents

The Gap Between Demo and Production for AI Agents

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Why Your AI Agent Fails on Edge Cases — And Why More Prompting Won't Fix It

Why Your AI Agent Fails on Edge Cases — And Why More Prompting Won't Fix It

Published on
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Ready to train your agent?

Get in touch and we'll help you get started with the right environment for your use case.

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