Getting Started¶

Welcome to the Agentic AI Nano-Degree! This guide will help you set up your environment and begin your learning journey.

Quick Start¶

Set Up Environment: Configure your development environment - see Setup & Environment
Choose Your Module: Start with Agent Frameworks, RAG Architecture, or MCP & Agent Protocols
Select Learning Path: Observer, Participant, or Implementer based on your time and depth needs
Begin Learning: Start with Session 0 of your chosen module

Prerequisites¶

Required Knowledge¶

Python Programming: Intermediate level (functions, classes, modules)
Command Line: Basic terminal/command prompt usage
Git: Basic version control operations
APIs: Understanding of REST APIs and HTTP

Recommended Background¶

Machine Learning: Basic understanding of ML concepts
Databases: Familiarity with SQL and NoSQL databases
Cloud Platforms: Exposure to AWS, GCP, or Azure
Docker: Basic containerization concepts

Hardware Requirements¶

RAM: Minimum 8GB, recommended 16GB+
Storage: 10GB free space for code examples and models
Internet: Stable connection for API calls and model downloads
OS: Windows 10+, macOS 10.15+, or Linux (Ubuntu 18.04+)

Course Overview¶

This nano-degree consists of three comprehensive modules:

Module 1: Agent Frameworks (10 Sessions)¶

Master the art of building intelligent agents using cutting-edge frameworks like LangChain, CrewAI, PydanticAI, and more.

Module 2: RAG Architecture (10 Sessions)¶

Build sophisticated Retrieval-Augmented Generation systems for enterprise applications with advanced chunking, vector databases, and evaluation techniques.

Module 3: MCP, ACP & Agent-to-Agent Communication (10 Sessions)¶

Master the Model Context Protocol, Agent Communication Protocol, and sophisticated multi-agent coordination patterns.

Learning Paths Guide¶

👀 Observer Path (30-80 min/session)¶

Best for: Quick understanding, decision makers, busy schedules

Activities:

Read core concepts and architectural overviews
Examine code examples and patterns
Understand design decisions and trade-offs
Review use cases and applications

Setup: Minimal - just read the materials

🙋‍♂️ Participant Path (50-110 min/session)¶

Best for: Hands-on learners, developers, practical understanding

Activities:

Follow guided implementations step-by-step
Run provided code examples
Complete exercises and mini-projects
Analyze and modify existing implementations

Setup: Full environment with API keys

🛠️ Implementer Path (120-260 min/session)¶

Best for: Deep expertise, architects, production focus

Activities:

Build complete systems from scratch
Implement custom components and optimizations
Explore advanced patterns and architectures
Create production-ready solutions

Setup: Full environment plus additional tools (Docker, cloud access)

🎧 Podcast Mode¶

Learn while commuting or multitasking! The nano-degree content can be converted to high-quality speech for audio learning.

Benefits of Podcast Mode¶

Flexible Learning: Study during commutes, workouts, or daily activities
Reinforcement: Audio learning complements visual reading
Accessibility: Support for learners with visual impairments
Review: Perfect for reviewing concepts while doing other activities

How to Use Podcast Mode¶

Text-to-Speech Tools: Use built-in OS features or dedicated apps
Browser Extensions: Install read-aloud extensions for web content
Mobile Apps: Use screen readers or text-to-speech apps
AI Assistants: Ask Claude, ChatGPT, or other AI to read content aloud

Recommended Tools¶

Desktop¶

macOS: Built-in VoiceOver or System Speech
Windows: Narrator or NVDA screen reader
Linux: eSpeak or Festival speech synthesis

Browser Extensions¶

Natural Reader: Chrome/Firefox extension with high-quality voices
Read Aloud: Free text-to-speech for web pages
SpeakIt: Simple and effective reading extension

Mobile Apps¶

Voice Dream Reader: Premium iOS/Android text-to-speech
NaturalReader: Mobile app with offline capabilities
Google TalkBack: Built-in Android accessibility feature

Tips for Audio Learning¶

Slow Down: Reduce speech speed for complex technical content
Take Breaks: Pause frequently to process information
Follow Along: Keep the text visible when possible
Review: Re-listen to challenging sections multiple times

Project Structure¶

nano-degree/
├── 00_intro/               # Course introduction and setup
│   ├── coder.md            # Development environment setup
│   ├── llmapi.md           # LLM API configuration
│   └── coder-detailed.md   # Detailed development setup
├── 01_frameworks/          # Agent Frameworks Module
│   ├── Session0_Introduction/
│   ├── Session1_Bare_Metal/
│   ├── ...
│   └── src/                # Source code examples
├── 02_rag/                 # RAG Architecture Module
│   ├── Session0_Introduction/
│   ├── Session1_Basic_RAG/
│   ├── ...
│   └── src/                # RAG implementations
├── 03_mcp-acp-a2a/         # MCP, ACP & A2A Module
│   ├── Session0_Introduction/
│   ├── Session1_Basic_MCP/
│   ├── ...
│   └── src/                # Protocol implementations
├── docs/                   # Documentation and guides
├── tests/                  # Test suites
├── requirements.txt        # Core dependencies
└── README.md               # Project overview

Study Recommendations¶

Time Management¶

Consistent Schedule: Set aside dedicated learning time
Session Completion: Finish sessions completely before moving on
Practice Time: Allow extra time for hands-on practice
Review Period: Revisit complex concepts after a few days

Learning Strategies¶

Start with Foundations: Don't skip early sessions
Practice Immediately: Run code examples as you learn
Take Notes: Document insights and modifications
Join Community: Engage with other learners
Apply Practically: Use concepts in real projects

Common Schedules¶

Intensive (2-3 weeks)¶

1 session every 2 days (👀 Observer path)
1 session per day (🙋‍♂️ Participant path)
2 sessions per day (🛠️ Implementer path)

Regular (4-6 weeks)¶

2-3 sessions per week (👀 Observer path)
1 session every 2 days (🙋‍♂️ Participant path)
1 session per day (🛠️ Implementer path)

Extended (8-12 weeks)¶

3-4 sessions per week (any path)
More time for practice and projects
Deep exploration of optional modules

Development Tools¶

Recommended IDEs¶

VS Code: Excellent Python support, extensions
PyCharm: Professional Python IDE
Jupyter: Great for experimentation and learning

Useful Extensions/Plugins¶

Python syntax highlighting and linting
Git integration
Docker support
Markdown preview
Code formatting (Black, autopep8)

Command Line Tools¶

# Code formatting
pip install black isort

# Linting
pip install flake8 pylint

# Testing
pip install pytest pytest-cov

# Documentation
pip install mkdocs mkdocs-material

🆘 Support & Community¶

Getting Help¶

Documentation: Check session materials and API docs first
GitHub Issues: Report bugs or unclear instructions
Discussions: Join community discussions for questions
Stack Overflow: Use tags agentic-ai, langchain, rag

Community Resources¶

GitHub Discussions: Course-specific questions and sharing
Discord/Slack: Real-time chat with other learners
Office Hours: Weekly community calls (check schedule)
Study Groups: Form groups with other learners

Troubleshooting¶

Common Issues¶

API Rate Limits: Use smaller examples, implement delays
Memory Issues: Close other applications, use smaller models
Import Errors: Check virtual environment activation
Network Issues: Verify API keys and internet connection

Debug Steps¶

Check error messages carefully
Verify environment variables
Test with minimal examples
Check API status pages
Ask for help with specific error details

Progress Tracking¶

Session Completion¶

Module Milestones¶

Session 0-2: Foundations established
Session 3-5: Intermediate concepts mastered
Session 6-8: Advanced patterns implemented
Session 9: Production deployment ready

Portfolio Projects¶

Consider building these throughout your learning:

Personal Assistant Agent (Sessions 1-4)
Document Q&A System (Sessions 1-3 RAG)
Multi-Agent Collaboration (Sessions 7-9)
Enterprise RAG System (Sessions 6-9 RAG)

Certification¶

Upon completion, you can:

Document Progress: Keep a portfolio of implementations
Share Projects: Publish code examples and write-ups
Join Alumni: Access to ongoing community and updates
Contribute: Help improve course materials

Ready to start learning? Choose your path and dive into the world of Agentic AI!

Start with Agent Frameworks → Begin with RAG Architecture → Master Agent Communication →