Edureka

Agentic AI Engineering 专项课程

Edureka

Agentic AI Engineering 专项课程

Master Agentic AI with LangChain, LangGraph & MCP. Build intelligent, stateful, and production-ready AI agents using modern frameworks and workflows.

Edureka

位教师:Edureka

包含在 Coursera Plus

深入学习学科知识
中级 等级

推荐体验

12 周 完成
在 6 小时 一周
灵活的计划
自行安排学习进度
深入学习学科知识
中级 等级

推荐体验

12 周 完成
在 6 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • Explain how agentic AI systems operate, including models, prompts, tools, and orchestration patterns used in modern AI applications.

  • Implement agent-based AI techniques to automate research, support, and decision workflows using tools and memory.

  • Assess agent workflows and outputs to improve reliability, correctness, observability, and system behavior.

  • Design and evaluate intelligent AI systems that follow responsible AI principles and align with technical and governance requirements.

要了解的详细信息

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授课语言:英语(English)
最近已更新!

February 2026

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  • 向大学和行业专家学习热门技能
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  • 通过 Edureka 获得职业证书

专业化 - 4门课程系列

您将学到什么

  • Define core principles of Agentic AI and LangChain ecosystem, including architecture and components.

  • Apply LangChain frameworks to set up AI environments and build intelligent agents.

  • Analyze prompt engineering, context design, and LCEL workflows to optimize agent behavior.

  • Design and evaluate multi-step agent workflows, integrating external tools to solve real-world tasks.

您将获得的技能

类别:Python Programming
类别:Gemini
类别:Generative AI
类别:Agentic Workflows
类别:Data Validation
类别:Retrieval-Augmented Generation
类别:Tool Calling
类别:Google Gemini
类别:AI Orchestration
类别:Artificial Intelligence
类别:Workflow Management
类别:Embeddings

您将学到什么

  • Design advanced workflows for intelligent agent systems with LangChain.

  • Apply multi-step reasoning and ReAct workflows to optimize AI agents.

  • Construct adaptive memory architectures and integrate multi-query retrieval.

  • Evaluate and apply error handling and output correction for pipeline reliability.

您将获得的技能

类别:Data Preprocessing
类别:Generative AI
类别:Artificial Intelligence
类别:Large Language Modeling
类别:Context Management
类别:Workflow Management
类别:Automation
类别:Data Transformation
类别:Responsible AI
类别:Metadata Management
类别:JSON
类别:Embeddings
Multi-Agent Systems with LangGraph

Multi-Agent Systems with LangGraph

第 3 门课程 11小时

您将学到什么

  • Explain how LangGraph executes workflows and manages state using reducers, typed state, and checkpoints.

  • Implement stateful agent pipelines with conditional routing, parallel execution, and recovery mechanisms.

  • Analyze agent behavior using execution logs, snapshots, and time-travel debugging techniques.

  • Design human-in-the-loop and multi-agent systems using supervision, planning, and consensus reasoning.

您将获得的技能

类别:Test Execution Engine
类别:Agentic Workflows
类别:Agentic systems
类别:LangGraph
类别:LLM Application
类别:AI Workflows
类别:LangChain
类别:Network Routing
类别:Debugging
类别:Human Factors (Security)
类别:Responsible AI
类别:Software Architecture
类别:Data Persistence
类别:Disaster Recovery
类别:AI Orchestration

您将学到什么

  • Explain MCP architecture and communication patterns that enable reliable, interoperable agentic AI systems.

  • Implement MCP servers, tools, and URI-based resources to connect agents with structured real-world data.

  • Design intelligent agents that reason reflexively, plan multi-step tasks, and recover from failures.

  • Deploy and evaluate agent systems using APIs, observability, monitoring, and scalable deployment practices.

您将获得的技能

类别:Application Programming Interface (API)
类别:Google Gemini
类别:Performance Analysis
类别:Middleware
类别:Python Programming
类别:Artificial Intelligence and Machine Learning (AI/ML)
类别:Agentic systems
类别:AI Workflows
类别:LangChain
类别:Artificial Intelligence
类别:Generative AI
类别:Interoperability
类别:LLM Application
类别:AI Orchestration
类别:LangGraph
类别:Scalability
类别:Model Context Protocol
类别:Prompt Engineering
类别:Application Deployment
类别:Model Deployment

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位教师

Edureka
Edureka
142 门课程 136,996 名学生

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Edureka

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