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AI Agents in Practice

(AI-AGENTS.AJ1) / ISBN : 979-8-90059-030-1
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1

Introduction

  • Who this course is for
  • What this course covers
  • To get the most out of this course
2

Evolution of GenAI Workflows

  • Understanding foundation models and the rise of LLMs
  • Latest significant breakthroughs
  • Road to AI agents
  • The need for an additional layer of intelligence: introducing AI agents
  • Summary
  • References
3

The Rise of AI Agents

  • Evolution of agents from RPA to AI agents
  • Components of an AI agent
  • Different types of AI agents
  • Summary
  • References
4

The Need for an AI Orchestrator

  • Introduction to AI orchestrators
  • Core components of an AI orchestrator
  • Overview of the most popular AI orchestrators in the market
  • How to choose the right orchestrator for your AI agent
  • Summary
  • References
5

The Need for Memory and Context Management

  • Different types of memory
  • Managing context windows
  • Storing, retrieving, and refreshing memory
  • Popular tools to manage memory
  • Summary
  • References
6

The Need for Tools and External Integrations

  • The anatomy of an AI agent’s tools
  • Hardcoded and semantic functions
  • APIs and web services
  • Databases and knowledge bases
  • Synchronous versus asynchronous calls
  • Summary
  • References
7

Building Your First AI Agent with LangChain

  • Introduction to the LangChain ecosystem
  • Overview of out-of-the-box components
  • Use case – e-commerce AI agent
  • Summary
  • References
8

Multi-Agent Applications

  • Introduction to multi-agent systems
  • Understanding and designing different workflows for your multi-agent system
  • Overview of multi-agent orchestrators
  • Building your first multi-agent application with LangGraph
  • Summary
  • References
9

Orchestrating Intelligence: Blueprint for Next-Gen Agent Protocols

  • What is a protocol?
  • Understanding the Model Context Protocol
  • Agent2Agent
  • Agent Commerce Protocol
  • Toward an agentic web
  • Summary
  • References
10

Navigating Ethical Challenges in Real-World AI

  • Ethical challenges in AI – fairness, transparency, privacy, and accountability
  • Agentic AI autonomy and its unique ethical challenges
  • Guardrails for safe and ethical AI
  • Content filtering and moderation in AI systems
  • Addressing the challenges: governance, regulations, and collaboration
  • Summary
  • References

1

Evolution of GenAI Workflows

  • Changing the Style of an Image Using ChatGPT
  • ChatGPT Reasoning Over a Puzzle
  • Using ChatGPT to Analyze an Image
2

The Rise of AI Agents

3

The Need for an AI Orchestrator

  • Exploring AI Orchestrators
4

The Need for Memory and Context Management

  • Understanding Few-Shot Prompting
5

The Need for Tools and External Integrations

  • Understanding Tools in AI Agents
6

Building Your First AI Agent with LangChain

  • Building the AskMamma agent
7

Multi-Agent Applications

  • Building the Multi-Agent Application with LangGraph
8

Navigating Ethical Challenges in Real-World AI

  • Understanding AI Protocols and the Agentic Web
  • Understanding Ethical Challenges in AI and Agentic Systems

AI Agents in Practice

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