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AI Agents in Practice

  This course teaches you to build and manage AI agents for practical, real-world problems, avoiding common deployment pitfalls.

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

AI Agents in Practice tackles the messy reality of getting agentic systems actually to work. It’s not just about chaining LLMs; it’s about what happens when they drift, forget context, or pick the wrong tool for the job. We dig into the components, the orchestrators, and the whole memory management problem.

You'll work through 11 Hands-on Labs, use 135 Practice Quizzes to solidify the ideas, and study 10 Comprehensive Chapters. This course won't make you an instant expert in every domain an agent might touch; that's impossible. Expect to get a better handle on the engineering tradeoffs. We also have 69 Flashcards, 57 Practice Exercises, and 69 Key Terms available.

Skills You’ll Get

 

  • Orchestrator Selection: Picking the wrong orchestrator means your agent won't scale or will constantly hit performance walls.
  • Memory Management: Agents will lose context or repeat actions without proper memory strategies, making them useless in complex tasks.
  • Tool Integration: Agents become isolated and incapable of real-world action if they can't effectively use external APIs or databases.
  • Multi-Agent Workflow Design: Without clear interaction protocols, multi-agent systems devolve into chaos, wasting compute and time.

Download Course Outline

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
  • Understanding AI Reasoning Through Puzzles
2

The Rise of AI Agents

  • Using ChatGPT to Analyze an Image
  • Understanding How AI Tutor Assistants Support Learning
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

Orchestrating Intelligence: Blueprint for Next-Gen Agent Protocols

  • Understanding AI Protocols and the Agentic Web
9

Navigating Ethical Challenges in Real-World AI

  • Understanding Ethical Challenges in AI and Agentic Systems

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We cover established, widely used frameworks like LangChain and LangGraph. The landscape shifts too fast to promise exhaustive coverage of every new release.

It assumes some programming familiarity, especially with Python. The labs are designed to guide you, but independent debugging often becomes necessary.

The course provides foundational understanding and practical build experience. Production readiness usually involves more robust testing, security hardening, and specific infrastructure considerations.

We use an e-commerce example to illustrate agent building. Applying agents to other business contexts requires adapting the principles to those specific domain challenges.

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