Best RL Environment Companies for Computer Use Agents in 2026
Patronus AI TeamNovember 20269 min read
The main companies building RL environments for computer use agents in 2026 are Patronus AI, Scale AI, Turing, HUD, Halluminate, Fleet, Plato and Browserbase. Some build finished environments on replicas of work software, some give teams a platform or simulator SDK to build their own, and Browserbase runs agents on live websites. To choose, decide whether your agent works from screenshots or from page structure, in a browser or on a desktop, then ask who wrote each task and how it is graded.
A computer use agent operates software the way a person does, by reading the screen, clicking and typing. It learns in an RL environment that gives it the application, a task and a grader that scores the result. Grading is hard here. A wrong click early can show up many steps later, and much work on a screen has no single right answer. This guide examines eight companies serving computer use buyers in 2026, starting with Patronus AI, a behavioral research lab that publishes at NeurIPS, ICML and ACL and whose open benchmark FinanceBench has 350+ citations as of November 2026.
The eight sit at different layers. Patronus AI is a research lab that builds environments, simulated apps and mock websites. Scale AI and Turing are data companies that sell computer use environments next to their human data work. Halluminate builds simulated websites and desktop environments for finance and consulting work, and Fleet builds training gyms where agents practice tasks under human supervision. HUD is a platform for building environments in-house, Plato provides VM-based app simulators through an SDK, and Browserbase supplies cloud browsers on live websites through BrowserEnv, its integration with Prime Intellect’s RL pipelines.
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Key takeaways
- Match the environment to how the agent sees. Agents that work from screenshots need pixel-level environments, and browser agents can also read the page structure. Browserbase’s BrowserEnv offers both modes, and Patronus AI’s environments serve both kinds of agent.
- Replicas and live sites trade off differently. Replicas of work software give repeatable state and clean resets, while live websites give realism and change under the agent. Turing sells interactive replicas of enterprise and consumer apps, and Browserbase runs agents on real websites.
- Desktop is its own requirement. Scale AI offers desktop VMs, Plato’s desktop environments run on an Ubuntu VM, and Patronus AI builds tasks in desktop software as well as the browser.
- Ask who wrote and checked each task. Much computer work has no single right answer, so tasks need people who do the work. Halluminate’s finance tasks are written and reviewed by deal professionals, and Patronus AI has experts complete each task by hand before it is used.
- Grade the end state, and audit the path. Plato scores the database and file changes a session made, HUD has agents audit every trace for grader mistakes and reward hacking, and Scale AI pairs rubrics with automated verifiers.
- Research depth is a useful filter. A builder that studies where agents fail is better placed to write tasks that teach. On Patronus AI’s BLUR (ACL 2025), people averaged 98% and the best system tested scored about 56%.
Comparison table
All eight companies at a glance, in the order of this guide. Security shows what each company lists on its own site. Sort by any column marked with arrows.
| # | Company | Best for | Type | What it sells | How tasks are graded | Security |
|---|---|---|---|---|---|---|
| 1 | Patronus AI | Work software in the browser and on the desktop, from a research lab | Research lab | Browser and desktop environments, simulated apps, mock websites | Tasks solved by experts first, graders checked in its own training | SOC 2, HIPAA, TISAX |
| 2 | Scale AI | Desktop VMs and web apps from a large data vendor | Data and evaluation | Web apps, desktop VMs, MCP servers | Expert rubrics and automated verifiers | SOC 2 Type II, ISO 27001, FedRAMP High |
| 3 | Turing | Replicas of named business apps | Expert data | UI clones and MCP backend environments | Not listed on its site | SOC 2 Type II |
| 4 | HUD | Building environments in-house | Platform | SDK and community environments | Agents audit every trace | SOC 2 Type I (Type II in progress) |
| 5 | Halluminate | Finance and consulting work | Specialist | Simulated websites and desktop environments | Deal professionals and mixed verifiers | Not listed on its site |
| 6 | Fleet | Simulated workplaces with human supervision | Specialist | Training gyms for agents | Human supervision in the simulation | Not listed on its site |
| 7 | Plato | Scripting against app simulators | Simulator SDK | VM-based app simulators | Database, file and output checks | Not listed on its site |
| 8 | Browserbase | Browser agents on live websites | Infrastructure | Cloud browsers wrapped as BrowserEnv | Set by each environment | SOC 2 Type II, HIPAA |
1. Patronus AI
Patronus AI is a behavioral research lab for AI. It builds RL environments for computer use agents on the kinds of software people use at work, alongside environments in alignment, coding, knowledge work, science and more.
How does Patronus AI work?
- Browser and desktop environments. Each attempt runs in its own sandbox with the real application, the agent’s tools and a grader. The same environment serves agents that work from screenshots and browser agents that read the page structure.
- Work software. One task family has agents use version control in the GitHub application through a browser, and its game development tasks mix code with work in a 3D editor.
- Tasks checked by people. Experts complete each task by hand to confirm it can be solved, and private details in the environments are made up for the task.
- Parts on their own. Simulated apps, mock websites and the data behind them can be bought separately.
Research
Patronus AI publishes on how models and agents behave and where they fail, with papers at NeurIPS 2026, ICML 2026 and ACL 2025. BLUR (ACL 2025) asks 573 questions that need search, reasoning and tool use. People averaged 98% on them, and the best system tested scored about 56%. FigmaTrace, an open dataset of 3,469 Figma design trajectories from 200+ hours of expert work, studies how people do creative work in software. Its researchers also train models on their own browser and desktop tasks to check that the tasks teach something (how they measured it).
Security
SOC 2, HIPAA and TISAX. Data ownership is set in each agreement.
Best for: Teams training computer use agents on work software in the browser and on the desktop, who want expert-checked tasks from a research lab.
2. Scale AI
Scale AI provides data, evaluations and RL environments to AI labs, governments and enterprises. Its computer use environments train agents to navigate and operate realistic desktop and web environments.
How does Scale AI work?
- Three environment types, web apps, desktop VMs including macOS-like and Windows-like systems, and MCP servers.
- Expert-designed objectives, rubrics and automated verifiers, with pass@k analysis of task complexity.
- Support for trajectories, state resets and examining rewards.
Where it fits
Teams that already buy data or evaluations from Scale AI can add computer use environments through the same relationship. Its security page lists a SOC 2 Type II report, ISO/IEC 27001:2022 and FedRAMP High authorization.
Best for: Teams that want desktop VMs and web apps from a large data vendor, or that need FedRAMP High.
3. Turing
Turing sells RL environments it describes as iterable UI and non-UI environments where agents learn long-horizon workflows, next to its expert data work.
How does Turing work?
- UI clones, interactive replicas of enterprise and consumer apps such as Jira, Salesforce and Zendesk.
- Backend MCP environments with policies, schemas and seed data built by subject matter experts.
- Harnesses that capture trajectory traces automatically.
Where it fits
Turing’s replicas suit teams whose agents must learn particular business applications by name.
Best for: Computer use agents that need replicas of common enterprise and consumer software.
4. HUD
HUD is a platform for building RL environments, with environments for browser and computer use, coding, deep research and more.
How does HUD work?
- Teams build environments with HUD’s SDK or start from community environments. Its site counts 18,000+ environments built.
- Agents audit every trace for grader mistakes and reward hacking.
- False positive, false negative and reward hacking detectors check each run’s grade and trace.
Where it fits
HUD suits teams that want to build computer use environments with their own experts on a shared platform.
Best for: Teams building their own environments, with trace audits built in.
5. Halluminate
Halluminate describes itself as a data research lab building benchmarks and RL environments for knowledge work, with a focus on financial services and consulting.
How does Halluminate work?
- Westworld, sandbox environments for training and evaluating computer use agents on realistic simulated websites.
- A finance diligence benchmark of 88 problems that runs inside desktop environments.
- Tasks written and reviewed by practicing deal professionals and checked by agentic, discrete and binary verifiers.
Where it fits
Halluminate’s environments center on finance and consulting, so its depth is in that work.
Best for: Computer use agents for finance and consulting work.
6. Fleet
Fleet creates simulated worlds and challenges to understand and shape the behavior of AI. It builds training gyms for agents, high-fidelity simulation environments where AI practices tasks and humans provide supervision.
How does Fleet work?
- High-fidelity simulation environments where agents practice tasks.
- Human supervision inside the simulation.
- Training gyms built with labs, hyperscalers and enterprises, as its homepage describes.
Where it fits
Fleet hopes its gyms lead to drop-in virtual assistants that know the software and tools of the teams they serve. Its public site does not list the applications its gyms cover, and its documentation needs a login. As Fleet AI, it sits on the technical committee of OpenEnv, an open environment interface published on Hugging Face.
Best for: Teams training agents as assistants inside simulated workplaces, with people supervising.
7. Plato
Plato provides VM-based simulators of applications, with sessions and test cases, through a Python SDK.
How does Plato work?
- Prebuilt simulators of apps such as a CRM, a Git server, a wiki and a Linux desktop.
- Desktop environments run on an Ubuntu VM, with pixel, mouse, keyboard, shell and file tools for the agent.
- Each session is scored against a test case, either by the database and file changes it made or by JSON output checked against a schema.
Where it fits
Plato suits engineering teams that want simulators to script tasks against.
Best for: Teams that want app simulators and state-based grading through an SDK.
8. Browserbase
Browserbase runs cloud browsers for agents. Its BrowserEnv wraps Browserbase sessions as an environment for training and evaluating browser agents with Prime Intellect’s RL pipelines.
How does Browserbase work?
- Agents work on real websites rather than replicas.
- A DOM mode, and a CUA mode that works from screenshots and coordinates with click, type, scroll and screenshot tools.
- Runs are orchestrated through the Prime CLI.
Where it fits
Browserbase is infrastructure, so tasks and graders come from each environment a team writes.
Best for: Teams training browser agents on live websites with an open RL stack.
Why Patronus AI stands out for computer use
Research depth
Patronus AI is a research lab first. Its 19 papers include work at NeurIPS, ICML and ACL. Its benchmarks are widely used. FinanceBench has 350+ citations and 81,000+ Hugging Face downloads of its open sample, and TRAIL, on errors in agent traces, has 110+ citations. In computer use, BLUR shows how far agents trail people on search and tool use, and FigmaTrace records how experts do design work in software.
Browser and desktop in one place
Tasks cover work in the browser and in desktop software, and the same environment serves screenshot agents and browser agents that read the page structure.
Tasks checked by people who do the work
Experts complete each task by hand to confirm it can be solved before it is used, and private details in the environments are made up for the task.
Enterprise-ready security
Patronus AI lists SOC 2, HIPAA and TISAX, with data ownership set in each agreement.
Built to fit the stack you have
Patronus AI confirms how tasks run on a team’s own training harness when it scopes the work, and simulated apps, mock websites and their data can be bought on their own.
For teams training agents to work in software built for people, Patronus AI builds the environments to train them on, backed by its research. Talk to Patronus AI about computer use environments.
Frequently asked questions
What is an RL environment for computer use agents?
It is a sandbox with an application, a task and a grader. The agent reads the screen or the page structure, clicks and types, and the grader scores the result. Our page on RL environments for computer use agents covers how Patronus AI builds them.
Which companies build RL environments for computer use agents?
Patronus AI, Scale AI, Turing, HUD, Halluminate, Fleet, Plato and Browserbase, among others. The table above sets out what each one sells and who it suits.
Should computer use environments use app replicas or live websites?
Replicas give repeatable state and clean resets, which makes grading reliable. Live websites give realism but change over time, so the same task can play out differently from one run to the next.
How are computer use tasks graded?
Many graders check the end state, such as the records, files or output the agent produced. Some also review the full trace for shortcuts and grader mistakes. Ask each vendor who wrote the grader and how it was tested against reward hacking.
Do computer use agents need desktop environments as well as browsers?
Yes, if the work happens in desktop software. Scale AI offers desktop VMs, Plato runs desktop environments on an Ubuntu VM, and Patronus AI builds tasks in desktop software, such as a 3D editor, as well as in the browser.
Should we build computer use environments in-house or buy them?
Build when your team has the engineers and the people who know the work, on a platform such as HUD or with Plato’s simulators. Buy when the software or the domain is specialized or time matters. Our guide to the best RL environment companies covers the wider field.
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