This course teaches you how to deploy fully functional, multi-agent AI systems using OpenAI’s latest tools and frameworks. You will learn how intelligent agents communicate, coordinate, and execute tasks together—then bring those capabilities into real-world applications through interactive interfaces and cloud deployment workflows.
Deploy AI Agents with OpenAI
即将结束： 只需 199 美元（原价 399 美元）即可通过 Coursera Plus 学习新技能。立即节省
您将获得的技能
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 Building AI Agents with OpenAI 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有3个模块
This module introduces the architecture and design principles behind building multi-agent personal assistant systems. Learners will explore the roles of planner, executor, knowledge, and interface agents and understand how these components collaborate through the Model Context Protocol (MCP). Through guided hands-on exercises with the AgentKit SDK, you’ll design modular frameworks, connect agents for shared context, and implement secure communication patterns that enable intelligent coordination and reliability across agent workflows.
涵盖的内容
12个视频4篇阅读材料4个作业
This module focuses on building user-facing, intelligent personal assistants that deliver seamless conversational experiences. You’ll learn to design intuitive chat interfaces using Streamlit, connect multi-agent backends via AgentKit sessions, and enable real-time streaming responses. The module also explores personalization strategies—storing user profiles, adapting behavior dynamically, and maintaining long-term context with MCP. Finally, you’ll implement automation by integrating external APIs and tools, enabling your assistant to execute real-world actions responsibly and efficiently.
涵盖的内容
12个视频3篇阅读材料4个作业
This module guides learners through validating, deploying, and scaling intelligent multi-agent personal assistant systems. You’ll begin by testing reasoning and coordination flows, writing structured test cases, and analyzing performance through response accuracy and latency metrics. Then, you’ll package and deploy your assistant using Streamlit Cloud, manage environment configurations, and enable secure, multi-agent sessions at scale. The module concludes with a capstone project where you’ll deploy a fully functional AI personal assistant, applying best practices for testing, documentation, and responsible AI deployment.
涵盖的内容
10个视频4篇阅读材料5个作业
获得职业证书
将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。
从 Software Development 浏览更多内容
状态：免费试用
状态：免费试用
状态：免费试用
状态：免费试用Coursera
人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
自 2018开始学习的学生
''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'
Jennifer J.
自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
Larry W.
自 2021开始学习的学生
''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'
Chaitanya A.
''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'
常见问题
The final phase focuses on testing, validating, and deploying multi-agent systems to ensure reliability, accuracy, scalability, and production readiness.
Yes. You will write structured test cases to validate reasoning steps, agent communication, MCP message exchanges, and overall workflow correctness.
Absolutely. You will measure response accuracy, latency, retrieval quality, and grounding strength to ensure agents behave consistently in real scenarios.
更多问题
提供助学金，
¹ 本课程的部分作业采用 AI 评分。对于这些作业，将根据 Coursera 隐私声明使用您的数据。