This course introduces the principles and practice of AI Agent Orchestration and Scaling, blending conceptual understanding with hands-on system design. You’ll learn how to coordinate, monitor, and optimize multiple AI agents that work together to deliver intelligent, autonomous workflows — with a special focus on building scalable customer support solutions powered by AI.


您将学到什么
Design orchestration frameworks that coordinate autonomous agents effectively.
Implement scaling strategies to manage high-performance, multi-agent systems.
Monitor and evaluate agent workflows to ensure consistency and reliability.
Develop autonomous AI agents that learn, adapt, and optimize over time.
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
- 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4个模块
This module explores multimodal AI and stateful orchestration using LangGraph to build intelligent, context-aware agents. You’ll learn to connect visual, textual, and API inputs for real-time problem diagnosis and decision-making. By the end, you’ll have built a visually informed, multi-tool triage agent capable of handling complex, multimodal workflows autonomously.
涵盖的内容
12个视频5篇阅读材料4个作业
This module focuses on enabling long-term memory and dynamic re-planning in autonomous agents. You’ll learn to design knowledge graphs and memory modules that let agents recall past experiences and adapt their actions. By the end, you’ll build a self-correcting, feedback-driven agent capable of real-time learning and continuous improvement through long-term memory integration.
涵盖的内容
10个视频4篇阅读材料4个作业
This module brings together orchestration, governance, and large-scale deployment of autonomous agents. You’ll implement guardrails, audit trails, and human-in-the-loop controls for safe operations, then deploy and scale workflows and containerization. By the end, you’ll have an end-to-end, production-ready autonomous system capable of governed, scalable decision-making.
涵盖的内容
11个视频4篇阅读材料4个作业
This module provides learners with an opportunity to synthesize their knowledge and demonstrate mastery of AI systems. Learners will review key concepts from memory-augmented agents, real-time data integration, multimodal orchestration, and governance frameworks. They will complete graded, scenario-based assessments to apply their understanding in building and managing collaborative, secure, and scalable agent ecosystems.
涵盖的内容
1个视频1篇阅读材料2个作业
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常见问题
The course aims to teach how to design, build, and scale autonomous agents that can reason, plan, and act using LangGraph integrating multimodal inputs, long-term memory, and dynamic orchestration for enterprise environments.
LangGraph extends LangChain by focusing on stateful orchestration — allowing developers to create graph-based agent workflows with persistent state, conditional routing, and memory-aware decision nodes.
Multimodal inputs (text, image, voice, etc.) enable agents to understand real-world contexts more accurately. For example, diagnosing issues from screenshots or combining text and visual data for richer reasoning.
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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。






