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NinjaTech AI专用虚拟机上的自治 AI 代理执行完整的工作

4.3 (4)
Daniel Nikulshyn审阅者 Daniel Nikulshyn·更新 2026年7月

概览

NinjaTech AI 提供自治的 AI 代理,它们在各自专用的虚拟计算机上运行,使它们能够浏览、编写代码、运行软件,并在没有持续用户监督的情况下完成最终交付物。每个代理像一个远程团队成员,拥有自己的沙盒环境来执行多步骤任务。 该平台面向需要超过聊天机器人功能的用户,专注于研究、应用原型设计、内容生产和工作流自动化等任务。代理可以被分配工作,独立执行,完成后即可检查结果。 NinjaTech 旨在为专业人士、开发者和小团队提供服务,帮助他们将耗时项目交给 AI 工作者执行,而不只是获得建议。

主要功能

  • 每个代理一个专用虚拟计算机
  • 自治的多步任务执行
  • 代码生成和部署
  • Web 浏览和研究功能
  • 文档创建和内容
  • 异步任务交付

价格

模型
Freemium
评分
4.3 / 5 (4)

使用场景

自治研究项目

分配代理浏览 web、收集资源并异步交付完成的研究摘要,而无需在每个步骤都进行手动监督。

应用原型设计和代码部署

让代理生成代码、在其专用 VM 上运行并产生工作原型,使开发人员能够一次性建造任务并交付。

在大规模内容生产中使用

将文档创建和内容工作委任给代理,完成工作交付,释放专业人士从耗时的写作中解放。

小团队的工作流自动化

使用自治代理作为远程队友来执行多步工作流,允许团队分配任务并在完成结果时检查回复。

优点 & 缺点

优点

  • 代理在专用虚拟机上运行
  • 自动处理多步任务
  • 交付完成工作,而不是仅仅是文本
  • 在编码、研究和内容方面都有用

缺点

  • 长时间运行的任务可能难以监测
  • 产出质量取决于任务复杂程度
  • 比手动工作流控件少
  • 成本随着代理使用量而增长

对决战绩

在万神殿中参与了 1 对决。

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Last battle

评测

4.3

4 个评分的平均值。

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Fatima Zahra

Fatima Zahra

Feb 17, 2026

Solid for our team

We rolled this out across the team last quarter and delivers completed work, not just text. Web browsing and research capabilities fits neatly into how we already work, and asynchronous task delivery removed a step we used to do by hand. but it has held up under daily use.

IB

Ingrid Bauer

Feb 12, 2026

Compared a few options

Evaluated this against two competitors. Where it wins: asynchronous task delivery and agents run on dedicated virtual machines. Where it lags: costs can scale with heavy agent usage. On balance the feature set — especially document and content creation — justifies the 4 stars for our use case.

GO

Grace Okafor

Dec 2, 2025

Use it every day

Honestly didn't expect to like it this much. Dedicated virtual computer per agent is exactly what I needed, and delivers completed work, not just text. I do wish output quality varies by task complexity, but I reach for it almost every day now and it just clicks.

Rina Desai

Rina Desai

Sep 27, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: code generation and deployment and handles multi-step tasks autonomously. Where it lags: less control than manual workflows. On balance the feature set — especially document and content creation — justifies the 4 stars for our use case.

问答

What is an AI workforce platform?

An AI workforce platform deploys autonomous, role-specific AI agents that execute complete business tasks end to end, not chatbots that answer questions or copilots that suggest edits. NinjaTech's AI workforce runs 24/7 on dedicated cloud computers, joins your Slack and Teams channels, acts across 3,000+ systems plus a full browser, and hands back finished work.

Asked by Nadia Benali · Jan 14, 2026

How is NinjaTech different from local desktop AI agents like Claude Cowork?

Local desktop agents run on your laptop, touch your local files, use one vendor's models, and pause when you do. NinjaTech runs in your own cloud or VPC on dedicated, isolated VMs, never touches your local machine, works with any model (Claude, GPT, Gemini, or open-source) and runs 24/7 on tasks that last hours to weeks.

Asked by Oscar Lindqvist · Jan 7, 2026

What does model-agnostic mean?

You choose the model for each agent: frontier LLMs like Claude, GPT, and Gemini for high-stakes reasoning, or top open-weight models where cost matters. Swap models anytime as the market evolves. Zero vendor lock-in. You can also bring your own model keys and contracts (BYOK).

Asked by Larisa Ionescu · Dec 28, 2025

Where does NinjaTech deploy?

Three ways: fully managed SaaS, ready on day one; deployed inside your own AWS, Azure, or GCP account, VPC, or data center (with an air-gapped option); or hybrid with bring-your-own-key. Your data never leaves your perimeter and is never used to train any model.

Asked by Ren Nakamura · Dec 14, 2025

How does unmetered pricing work?

With the deployed option, the full platform runs on GPUs you control. You pay the GPU-hour, never per token, per task, or per seat. Run unlimited users and parallel agents; cost is capped by hardware. It is 5-10x cheaper than per-token frontier APIs for the same workload, and the spend counts toward your Azure MACC, AWS EDP, or Google Cloud CUD commitment.

Asked by Naomi Suzuki · Nov 3, 2025

提问

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