Agent in Depth

Day 1: Agent Theory and System Design

Systematic Understanding from Concept to Architecture

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📚 Today's Content

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1.1 Agent Concepts and Core Composition

Agent = LLM + Memory + Tools + Knowledge

Explain the composition and operation mechanism of Agent based on LangChain / LangGraph

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1.2 Analysis of Mainstream Agent Models

ReAct model: a cycle mechanism of reasoning and action

Function Call: structured tool interaction

MCP (Modular Command Protocol): modular command protocol

A2A (Agent-to-Agent): inter-agent communication protocol

Analysis of applicable scenarios, advantages and limitations of each model

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1.3 Agent System Design Methodology

Deconstruct the Agent workflow and draw a flowchart

Methodology for designing an Agentic system from 0:

• Architectural design principles

• Module division strategy

• Interface definition specification

Demonstrate process modeling based on LangGraph

💡 Learning Objectives

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✓ In-depth understanding of the core composition and working principle of Agent
✓ Master the design ideas and implementation methods of mainstream Agent models
✓ Possess the architectural design capabilities of Agent systems
✓ Proficient in using LangGraph for process modeling

Day 2: Basic Practice and LangGraph Application

Skills Transformation from Theory to Practice

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📚 Today's Content

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2.1 LangGraph Core Principles

Basic concepts and core modules of LangGraph:

• Agent node: intelligent decision-making and reasoning

• Tool node: external capability integration

• Memory node: context and state management

Model a typical Agent flowchart

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2.2 Practice: Basic Agent Implementation

ReAct Agent construction

Agent + Tools (Function Call) integration

Agent + MCP:

• Introduction to MCP protocol

• Build MCP Server

• Implement integration of Agent and MCP Server

💡 Learning Objectives

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✓ Proficient in the core concepts and usage of LangGraph
✓ Ability to independently build a basic Agent system
✓ Master the integration and development technology of Agent and tools
✓ Understand and implement Agent integration of MCP protocol

Day 3: Advanced Practice and Multi-Agent Systems

Complex System Design and Collaboration Mechanisms

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📚 Today's Content

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3.1 Advanced Capabilities Expansion of Agent

Memory & Knowledge Integration:

• Short-term and long-term memory mechanisms

• External knowledge access and Agentic RAG architecture

Human-in-the-Loop Design:

• Introduce human feedback mechanism and interaction design

• Design controllable and debuggable Agent behavior

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3.2 Multi-Agent System Design and Collaboration Mechanism

Typical architectural patterns of multi-Agent collaboration

Detailed explanation of A2A (Agent-to-Agent) protocol:

• What is A2A

• Communication and collaboration mechanism between Agents

• Allocation and control flow of collaborative tasks

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3.3 Comprehensive Project Practice

Design a multi-Agent collaboration system from requirements to deployment

Use LangGraph for modeling, debugging and demonstration

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3.4 Course Summary and Extension

Course review and frequently asked questions

Suggestions for subsequent learning paths and reference materials

💡 Learning Objectives

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✓ Master the design and implementation of advanced Agent capabilities
✓ Possess the architectural design capabilities of multi-agent systems
✓ Proficient in using A2A protocol for Agent collaboration
✓ Complete an end-to-end multi-Agent system project
✓ Possess the ability to independently design and deploy Agentic applications

Syllabus

Day 1: Agent Theory and System Design

Day 2: Basic Practice and LangGraph Application

Day 3: Advanced Practice and Multi-Agent Systems

📦 Appendix

Day 1: Agent Theory and System Design

📚 Today's Content

1.1 Agent Concepts and Core Composition

Agent = LLM + Memory + Tools + Knowledge

Explain the composition and operation mechanism of Agent based on LangChain / LangGraph

1.2 Analysis of Mainstream Agent Models

ReAct model: a cycle mechanism of reasoning and action

Function Call: structured tool interaction

MCP (Modular Command Protocol): modular command protocol

A2A (Agent-to-Agent): inter-agent communication protocol

Analysis of applicable scenarios, advantages and limitations of each model

1.3 Agent System Design Methodology

Deconstruct the Agent workflow and draw a flowchart

Methodology for designing an Agentic system from 0:

Demonstrate process modeling based on LangGraph

💡 Learning Objectives

Day 2: Basic Practice and LangGraph Application

📚 Today's Content

2.1 LangGraph Core Principles

Basic concepts and core modules of LangGraph:

Model a typical Agent flowchart

2.2 Practice: Basic Agent Implementation

ReAct Agent construction

Agent + Tools (Function Call) integration

Agent + MCP:

💡 Learning Objectives

Day 3: Advanced Practice and Multi-Agent Systems

📚 Today's Content

3.1 Advanced Capabilities Expansion of Agent

Memory & Knowledge Integration:

Human-in-the-Loop Design:

3.2 Multi-Agent System Design and Collaboration Mechanism

Typical architectural patterns of multi-Agent collaboration

Detailed explanation of A2A (Agent-to-Agent) protocol:

3.3 Comprehensive Project Practice

Design a multi-Agent collaboration system from requirements to deployment

Use LangGraph for modeling, debugging and demonstration

3.4 Course Summary and Extension

Course review and frequently asked questions

Suggestions for subsequent learning paths and reference materials

💡 Learning Objectives

📦 Appendix