AI-AGENTS.AJ1
AI Agents in Practice
This course teaches you to build and manage AI agents for practical, real-world problems, avoiding common deployment pitfalls.
- Practice in 33 Hands-On Labs — nothing to install
- 10 Interactive Lessons and 58 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
33 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Skills you'll get
What you will be able to do
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.
- 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.
Course Highlights
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10 Structured Lessons Comprehensive coverage of core course objectives
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33 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
10 Interactive Lessons · 58 topics01 Introduction 3 topics +
- Who this course is for
- What this course covers
- To get the most out of this course
02 Evolution of GenAI Workflows 6 topics · 7 LiveLab +
- 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
7 LiveLab in this lesson — see the labs panel →
03 The Rise of AI Agents 5 topics · 1 LiveLab +
- Evolution of agents from RPA to AI agents
- Components of an AI agent
- Different types of AI agents
- Summary
- References
1 LiveLab in this lesson — see the labs panel →
04 The Need for an AI Orchestrator 6 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
05 The Need for Memory and Context Management 6 topics · 4 LiveLab +
- Different types of memory
- Managing context windows
- Storing, retrieving, and refreshing memory
- Popular tools to manage memory
- Summary
- References
4 LiveLab in this lesson — see the labs panel →
06 The Need for Tools and External Integrations 7 topics · 5 LiveLab +
- 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
5 LiveLab in this lesson — see the labs panel →
07 Building Your First AI Agent with LangChain 5 topics · 6 LiveLab +
- Introduction to the LangChain ecosystem
- Overview of out-of-the-box components
- Use case – e-commerce AI agent
- Summary
- References
6 LiveLab in this lesson — see the labs panel →
08 Multi-Agent Applications 6 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
09 Orchestrating Intelligence: Blueprint for Next-Gen Agent Protocols 7 topics · 4 LiveLab +
- What is a protocol?
- Understanding the Model Context Protocol
- Agent2Agent
- Agent Commerce Protocol
- Toward an agentic web
- Summary
- References
4 LiveLab in this lesson — see the labs panel →
10 Navigating Ethical Challenges in Real-World AI 7 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
33 LiveLabs- Building a Lightweight Agent with a SLM
- Building a Conversational AI Agent
- Using ChatGPT to Analyze an Image
- Changing the Style of an Image Using ChatGPT
- Understanding AI Reasoning Through Puzzles
- Implementing Task Automation Agents
- ChatGPT Reasoning Over a Puzzle
- Understanding How AI Tutor Assistants Support Learning
- Integrating External APIs and Tools into Agents
- Building a Customer Support AI Agent with LangChain
- Exploring AI Orchestrators
- Implementing Short-Term Memory in an AI Agent
- Understanding Few-Shot Prompting
- Implementing Temporal Reasoning in Conversational Agents
- Building a Temperature Conversion Tool for AI Agents
- Building AI Agents with Web APIs
- Designing Agentic RAG Systems with Tool-Based Retrieval
- Implementing Synchronous and Asynchronous Agent Tool Calls
- Understanding Tools in AI Agents
- Integrating LLMs with LangChain Open Source Framework
- Designing Modular AI Systems Using Build-Time Logic
- Creating a Knowledge Retrieval Agent with Vector Search
- Managing Agent Reasoning with Agent Executors
- Building the AskMamma Agent
- Building the CalculatorAssistant AI Agent
- Designing Conversational Multi-Agent Systems Using AutoGen
- Building a Multi-Agent Application with LangGraph
- Designing Agent2Agent Communication Protocols for Multi-Agent Systems
- Designing Agent Discoverability Using Agent Cards
- Implementing NLWeb Endpoints for AI Agents
- Understanding AI Protocols and the Agentic Web
- Implementing Content Filtering and Moderation Systems
- Understanding Ethical Challenges in AI and Agentic Systems
03 / FAQs
Questions before you start
Is this course going to cover all the latest agent frameworks, the really new ones?+
I'm not a senior developer; will I struggle with the technical depth?+
Can I use these agents directly in production after completing the course?+
Does this course teach specific business use cases for agents?+
Keep going
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Build Agents That Actually Work
Stop chasing hype and start managing the trade-offs. Enroll now to master orchestrators, memory, and tool integration through 11 hands-on labs.
- 1 year of full access
- 33 LiveLab included
- Certificate of completion
No credit card required