logo
  • 环境
  • 企业版
  • 价格

Continuous Evals to Train Agents for
Your Workflows

We build reproducible environments, benchmark suites, and training loops so Eigent improves reliably on your enterprise-grade systems in browser and terminal scenarios.

Step 1

Environment Design

Environment Design
Step 2

RL Training Loop

RL Training Loop
Step 3

Agent Performance

Agent Performance

Why Environments Matter for Agents

Agentic enterprise automation is shifting from pure text training to real interactions with business systems. To improve reliability, you need reproducible environments to train and validate agent behavior, plus an end-to-end evaluation loop for continuous measurement and iteration. These environments also enable scenario-specific model training—so you can deploy agents (and models) optimized for your enterprise workflows.

What We Deliver

Enterprise-grade environments, evaluations, training loops built for real systems.

Real-world Benchmarks Built from Enterprise Scenarios
A Reproducible RL environment for Ongoing Training
Training Loop for improving Model Performance in Real-World Environments
Better-performing, more Adaptive Eigent Performance in Your Scenario

Use Cases

Checkout our use cases for our clients.

CRM / ERPBrowser Automation

RL Environment for Browser Automation (CRM / ERP / E-commerce)

We built a suite of reproducible browser-based RL environments for ERP/CRM and e-commerce back-office workflows, paired with 2,000+ real-world task samples for training and evaluating automated task execution models across multi-page enterprise systems.

  • Environments: ERP / CRM / e-commerce management platforms
  • Tasks: single-step search, multi-step chained search, cross-table lookup, data analysis, edit and creation operations (leads/orders/invoices)
RL Environment for Browser Automation
Terminal Environment for Terminal Agents
Linux / UbuntuTerminal Agents

Terminal Environment for Terminal Use

We built a dataset designed to improve terminal agent capabilities, specifically for training and evaluating models on task execution in real Linux/Ubuntu environments. Each entry corresponds to a reproducible containerized environment and a complete terminal task package.

  • Environments: Linux/Ubuntu terminal environments
  • Tasks: tool usage, multi-step operations, and error handling

Trusted by Selected Enterprise Teams

Reference deployments available upon request.
FinanceE-commerceLogisticsIT OperationsCustomer SupportR&D

Build environments that make your agents reliable

Title*
Full Name*
Job Title*
Business Email*
Company Name*
Country/Region*
What types of data or environments are you interested in? * (Select all that apply)
Additional Details about Your Request

Open Source Cowork 桌面版

Eigent 是一款开源产品，这意味着你可以使用自己的 API 密钥或本地模型免费自行托管。

联系销售
Eigent

获取关于 AI 员工自动化的最新更新和教程。

产品Eigent环境定价企业版
探索解决方案使用场景技能插件博客
开发者文档GitHubCAMEL-AI开源基金合作伙伴
下载开源版
公司关于我们品牌加入我们使用条款隐私政策安全与信任Cookie 政策退款与试用政策

保留所有权利 © 2026 EIGENT UK LTD

Eigent 1.0 新版本发布！download