TechCrunch News 10月07日
OpenAI发布AgentKit,简化AI代理开发与部署
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OpenAI在Dev Day活动上推出了AgentKit,这是一个旨在简化AI代理构建和部署的工具集。AgentKit提供了完整的构建模块,帮助开发者将AI代理从原型快速推向生产环境,减少摩擦。此举标志着OpenAI致力于通过加速和简化代理开发来提高开发者采用率,并与竞争对手在提供企业级自主代理工具方面展开竞争。AgentKit包含Agent Builder、ChatKit、Evals for Agents以及连接器注册表等核心功能,让开发者能更便捷地设计代理逻辑、集成聊天体验、评估性能并连接外部系统。

🚀 AgentKit的发布旨在降低AI代理开发的门槛,提供一套完整的工具集,让开发者能够更快速、更便捷地将AI代理从概念验证阶段推向实际生产应用,显著减少开发过程中的阻力。

💡 AgentKit包含多个核心组件,如Agent Builder,一个类似Canva的可视化工具,用于快速设计代理的逻辑和步骤;ChatKit,提供可嵌入的聊天界面,便于开发者将对话体验集成到现有应用中。

📊 Evals for Agents提供了衡量AI代理性能的工具,包括分步追踪评分、评估单个代理组件的数据集、自动化提示优化以及直接在OpenAI平台上对外部模型进行评估的能力。

🔗 AgentKit还集成了OpenAI的连接器注册表,允许开发者通过“管理控制面板”安全地将代理连接到内部工具和第三方系统,同时确保安全性和控制权。

✅ 整个AgentKit的易用性得到了现场演示,一名OpenAI工程师在不到八分钟的时间内成功构建了完整的AI工作流和两个AI代理,凸显了该工具集的设计目标是提供开发者在早期构建代理时所期望的功能。

OpenAI CEO Sam Altman on Monday announced the launch of AgentKit, a toolkit for building and deploying AI agents, at the firm’s Dev Day event.  

“AgentKit is a complete set of building blocks available in the open AI platform designed to help you take agents from prototype to production. It is everything you need to build, deploy, and optimize agent workflows with way less friction,” Altman said.  

The launch highlights OpenAI’s push to increase developer adoption by making agent building faster and easier. It also signals a competitive move against other AI platforms racing to offer integrated tools for building autonomous agents for enterprises that can perform complex tasks, not just respond to prompts.

AgentKit was one of several announcements at OpenAI’s Dev Day, including the launch of the ability to build apps directly inside ChatGPT, which has hit 800 million weekly active users.  

AgentKit includes a few core capabilities. The first is Agent Builder, which Altman described as like Canva for building agents.  

“It’s a fast, visual way to design the logic, steps, ideas,” Altman said. “It’s built on top of the responses API that hundreds of thousands of developers already use.” 

The second capability of AgentKit is ChatKit, which provides a simple embeddable chat interface that developers can use to bring chat experiences into their own apps.  

“You can bring your own brand, your own workflows, whatever makes your own product unique,” Altman said. 

Evals for Agents introduces tools to measure AI agent performance, including step-by-step trace grading, datasets for assessing individual agent components, automated prompt optimization, and the ability to run evaluations on external models directly from the OpenAI platform. 

Finally, AgentKit includes access to OpenAI’s connector registry, so developers can securely connect agents to internal tools and third-party systems through an “admin control panel” while maintaining security and control. 

To prove how easy it is to use AgentKit, Christina Huang, an OpenAI engineer, built an entire AI workflow and two AI agents live onstage in under eight minutes.  

“This is all the stuff that we wished we had when we were trying to build our first agents,” Altman said, noting that OpenAI has already signed on several launch partners that have already scaled agents using AgentKit. 

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