Multi-agent systems let generative AI go beyond single tasks, enabling teams of agents that can plan, reason, and collaborate to solve complex problems.
Design, Develop, and Deploy Multi-Agent Systems with CrewAI
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您将学到什么
Build practical multi-agent systems that collaborate, use tools and memory, and scale reliably to production.
Use tools such as web search and MCP servers to extend your agents’ real-world capabilities.
Add guardrails, hooks, and low level control with CrewAI Flows to make AI systems safer, more predictable, and production-ready.
您将获得的技能
- Agentic systems
- Automation
- User Feedback
- Generative AI Agents
- Continuous Monitoring
- Workflow Management
- Artificial Intelligence and Machine Learning (AI/ML)
- LLM Application
- Test Tools
- Tool Calling
- System Monitoring
- Integration Testing
- Performance Metric
- Artificial Intelligence
- Application Performance Management
- Code Review
- Scalability
- Business Software
- Prompt Engineering
要了解的详细信息
可分享的证书
添加到您的领英档案
最近已更新！
November 2025
作业
4 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
该课程共有4个模块
In this module, you will design and develop single- and multi-agent systems from concept to prototype. You'll tune agent behavior using context engineering, study real-world use cases, and examine how these systems run in production. You will complete the graded quiz and Automatic Code Review graded programming assignment to pass the module.
涵盖的内容
10个视频3篇阅读材料1个作业1个编程作业2个非评分实验室
In this module, you'll learn to control agent behavior with guardrails, execution hooks, memory, and knowledge to guide richer decision cycles. You will build and integrate custom tools, and learn how the Model Context Protocol is expanding agent capabilities. You will complete the graded quiz and the Automatic Code Review graded programming assignment to pass the module.
涵盖的内容
7个视频1篇阅读材料1个作业1个编程作业2个非评分实验室
In this module, you will orchestrate agents in complex coordination pattern using sequential, parallel, hierarchical, hybrid, and async processes. You'll also implement Flows as a low-level control layer for orchestration. Finally, you'll learn how to monitor multi-agent systems with tracing, sampling, and observability tools, as well as train agents using human-in-the-loop feedback and structured evaluations. You will complete the graded quiz and Automatic Code Review Flow graded programming assignment to pass the module.
涵盖的内容
7个视频1篇阅读材料1个作业1个编程作业2个非评分实验室
In this module, you will explore how businesses adopt agents across industries and functions, from early chatbots to workflow co-pilots. You will analyze real deployments through case study interviews featuring leaders from Exa, Snyk, Weaviate, and AB InBev. You will complete the graded quiz to pass the module.
涵盖的内容
8个视频2篇阅读材料1个作业
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状态：免费DeepLearning.AI
状态：免费试用
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