Agentic AI Nanodegree

Advanced AI Agent Development & Protocol Integration - A comprehensive 6-week self-paced program covering modern AI agent development, from foundational patterns through cutting-edge protocols and production deployment.

🔒 Corporate Content Security Notice: This repository contains only public course materials. BMW-specific corporate content is delivered securely through automatic network detection and encryption - no sensitive corporate information is stored in this public repository or exposed outside BMW corporate networks.

🚀 Quick Start

Start Here: Cloud Development Environment - Access your pre-configured workspace at http://10.21.202.14/workspaces

Essential Setup:

1. Cloud Environment Setup - Pre-configured workspace with all dependencies
2. LLM API Configuration - Gaia API access for AI models
3. 🎧 Podcast Mode - Learn hands-free while commuting

Course Structure

3 Modules × 2 Weeks Each = 6 Weeks Total

Module 01: Agent Frameworks & Patterns

Focus: Core agent development with modern frameworks
Start Module 01 →

• Week 1: Bare metal agents, LangChain, LangGraph, CrewAI, PydanticAI
• Week 2: Atomic Agents, ADK, Agno, Multi-agent patterns, Production deployment

Module 02: Retrieval-Augmented Generation (RAG)

Focus: Advanced RAG systems and cognitive architectures
Start Module 02 →

• Week 1: Basic RAG, chunking, vector databases, query enhancement, evaluation
• Week 2: Graph-based RAG, agentic RAG, multimodal RAG, production integration

Module 03: MCP, ACP & A2A Communication

Focus: Agent communication protocols and distributed systems
Start Module 03 →

• Week 1: MCP server basics, filesystem integration, LangChain integration, production deployment
• Week 2: Security, ACP fundamentals, A2A communication, advanced workflows, enterprise integration

Learning Paths

Each session offers 3 learning paths to match your time and depth preferences:

• Observer Path (30-50 min): Conceptual understanding and overview
• Participant Path (60-90 min): Guided implementation with examples
• Implementer Path (120-180 min): Complete hands-on development

Learning Outcomes

By completion, you will:

• Master the five core agentic patterns: Reflection, Tool Use, ReAct, Planning, Multi-Agent Collaboration
• Build production-ready agents using cutting-edge frameworks (LangChain, CrewAI, PydanticAI, Atomic Agents, Agno)
• Implement sophisticated RAG systems with NodeRAG, reasoning-augmented retrieval, and multimodal capabilities
• Design distributed agent architectures using MCP, ACP, and A2A protocols
• Deploy enterprise-grade agent systems with monitoring, security, and scalability

Prerequisites

Required:

• Python programming (intermediate level)
• API integration experience (REST APIs, JSON)
• Software design understanding (OOP, design patterns)
• Development environment familiarity (virtual environments, package management)

Recommended:

• Basic LLM understanding
• HTTP protocols and web services experience
• Database and data processing knowledge
• Distributed systems concepts

Getting Started

Cloud Environment (Recommended)

Access your pre-configured workspace - no local installation needed!

1. Access Coder Workspace - http://10.21.202.14/workspaces
2. Configure LLM API - Gaia API setup
3. Enable Podcast Mode - Learn while commuting
4. Choose your learning path and start with Module 01

Local Setup (Alternative)

Public Users (Standard Clone)

git clone https://github.com/fwornle/agentic-ai-nano.git
cd agentic-ai-nano
python -m venv venv
source venv/bin/activate
pip install -r docs-content/01_frameworks/src/session1/requirements.txt
mkdocs serve  # View documentation locally

Corporate Users (With Corporate Content)

# Clone with corporate content submodule
git clone --recurse-submodules https://github.com/fwornle/agentic-ai-nano.git
cd agentic-ai-nano
python -m venv venv
source venv/bin/activate
pip install -r docs-content/01_frameworks/src/session1/requirements.txt
mkdocs serve  # View documentation with corporate content

Alternative for Corporate Users (if submodules weren't cloned initially):

git clone https://github.com/fwornle/agentic-ai-nano.git
cd agentic-ai-nano
git submodule init
git submodule update  # Fetch corporate content
# Continue with setup...

Navigation

Documentation

• Interactive Documentation - Full browsing experience with search
• Live Demo Site - GitHub Pages deployment

Module Quick Access

• Module 01: Frameworks - Agent patterns and frameworks
• Module 02: RAG - Advanced retrieval and reasoning systems
• Module 03: Protocols - Agent communication and integration

Resources

• Source Code Examples - Complete implementations
• Architecture Diagrams - PlantUML system diagrams
• Podcast Feature Guide - Hands-free learning setup

Corporate Content Availability

This nanodegree features automatic corporate network (CN) detection that securely unlocks additional BMW-specific content when accessed from BMW corporate networks. Important: Sensitive corporate content is never stored in this public repository or exposed outside the BMW corporate network.

How Secure Content Access Works

🌐 Public Network Access:

• Generic course materials suitable for all audiences
• Local development environment setup guides
• Public LLM API configuration (OpenAI, Anthropic, etc.)
• Standard 9-session course structure per module

🏢 BMW Corporate Network Access (Secure):

• Automatic Detection: System detects corporate network via IP ranges and internal service accessibility
• Encrypted Content Delivery: Corporate content is encrypted and only decryptable from BMW networks
• Enhanced Content: BMW-specific examples, configurations, and deployment guides
• Additional Sessions: Access to Session 10 "Enterprise Integration & Production Deployment"
• Cloud Development Environment: Pre-configured BMW Coder workspace access
• BMW Gaia LLM API: Internal LLM service integration
• Corporate Infrastructure: BMW-specific architecture diagrams and integration patterns

Security & Content Protection

🔒 Content Security Model:

• No Sensitive Data in Repository: Corporate content is never stored in plaintext in this public repository
• Encrypted Distribution: Corporate content is AES-256-GCM encrypted before inclusion in public deployments
• Network-Based Decryption: Content can only be decrypted and viewed from BMW corporate networks
• Automatic Fallback: Public users always see appropriate generic content without corporate details

Network Detection Indicators

When you access the course, you'll see visual indicators of your network status:

• 🏢 BMW Corporate Network: Blue indicator shows cloud development environment is available
• 🌐 Public Network: Green indicator shows local setup is required
• 🔍 Detecting Network...: Orange indicator during network detection process

Repository Access Models

Public Users (Standard Access):

git clone https://github.com/fwornle/agentic-ai-nano.git
# No additional setup needed - corporate content loads automatically if on BMW network

Corporate Users (Local Development):

# Option 1: Clone with corporate submodule for local development
git clone --recurse-submodules https://github.com/fwornle/agentic-ai-nano.git

# Option 2: Add corporate submodule to existing clone
git clone https://github.com/fwornle/agentic-ai-nano.git
cd agentic-ai-nano
git submodule init && git submodule update

Corporate Content Detection Details

Network Detection Process:

1. Hostname Detection: Checks for *.bmw.com, *.bmwgroup.com, and internal IP ranges
2. External IP Verification: Validates against BMW IP ranges (160.46., 194.114., etc.)
3. Internal Service Test: Tests accessibility to contenthub.bmwgroup.net
4. Automatic Fallback: Gracefully falls back to public content if detection fails

What Corporate Users Get:

Enhanced Module Content:

• Module 01: BMW Coder cloud environment setup and integration
• Module 02: Corporate-specific RAG examples with internal data sources
• Module 03: Session 10 "Enterprise Integration & Production Deployment"
  • BMW-specific infrastructure patterns
  • Corporate deployment strategies
  • Enterprise security considerations
  • Production monitoring and scaling

Corporate-Specific Features:

• Pre-configured Development Environment: http://10.21.202.14/workspaces
• BMW Gaia LLM API Access: Internal model endpoints and authentication
• Corporate Architecture Diagrams: BMW-specific system integration patterns
• Enterprise Security Examples: Corporate authentication and authorization patterns

Architecture Overview

Intelligent Content System:

• Automatic Detection: No manual switches or configuration required
• Secure Content Delivery: Corporate content encrypted and decrypted client-side
• Seamless Experience: Users see appropriate content based on their network automatically
• Graceful Degradation: Always falls back to public content to ensure accessibility

Technical Components:

• Network Detection Engine: Multi-layer corporate network detection
• Content Encryption System: AES-GCM encrypted corporate content for secure public deployment
• Dynamic Content Loader: Client-side decryption and content injection
• Navigation Intelligence: Automatic addition/removal of corporate navigation items

Content Structure

nano-degree/ (Public Repository)
├── docs-content/
│   ├── 00_intro/coder.md            # Generic setup guide (corporate content injected dynamically)
│   ├── 01_frameworks/               # Public course content
│   ├── 02_rag/                      # Public course content  
│   ├── 03_mcp-acp-a2a/             # Public course content (Session 10 added dynamically)
│   └── javascripts/
│       ├── network-detection-unified.js  # Network detection & content injection
│       └── corporate-content-loader.js   # Legacy loader (deprecated)
├── scripts/
│   ├── encrypt-corporate-content.js      # Content encryption utility
│   └── inject-corporate-content.js       # Build-time content injection
└── site/                            # Built site (contains encrypted corporate content in HTML comments)
    └── **/*.html                    # Pages with encrypted content embedded for CN decryption

corporate-only/ (Private Repository - Referenced as Submodule)
├── content.encrypted.json           # AES-256-GCM encrypted content bundle
├── 00_intro/
│   ├── coder-concise.md             # Compact BMW Coder setup guide  
│   ├── coder-detailed.md            # Detailed BMW environment setup
│   └── llmapi-detailed.md           # BMW Gaia LLM API documentation
├── 03_mcp-acp-a2a/
│   └── Session10_Enterprise_Integration_Production_Deployment.md
└── images/                          # BMW-specific diagrams (encrypted)
    ├── bmw-*.png                    # Corporate architecture diagrams
    ├── coder-*.png                  # Development environment screenshots
    └── cn-*.png                     # Corporate network diagrams

Security Note: The corporate-only/ directory is a private Git submodule. Corporate content is encrypted before being embedded in the public deployment and can only be decrypted from BMW corporate networks.

Content Publishing Workflow

For Public Deployment:

1. Corporate content encrypted using AES-256-GCM
2. Encrypted manifest included in public repository
3. Network detection determines content visibility
4. Corporate content decrypted client-side on corporate network

For Corporate Environment:

• Direct access to unencrypted corporate content
• Corporate cloud development environment
• Pre-configured templates and organization-specific integrations

Development Workflow

For Corporate Content Maintainers (BMW Internal Only):

# Note: This workflow requires access to the private corporate-only repository

# 1. Update corporate content (private repository)
cd docs-content/corporate-only
# Make changes to corporate files
git add . && git commit -m "Update corporate content"
git push origin main

# 2. Encrypt for public deployment (main repository)
cd ../../
node scripts/encrypt-corporate-content.js

# 3. Deploy encrypted content
git add docs-content/corporate-only
git commit -m "Update encrypted corporate content"
git push origin main

# 4. Test from corporate network
# Access deployed site - decryption tested automatically

For Public Contributors:

# Standard contribution workflow - no access to corporate content required
git clone https://github.com/fwornle/agentic-ai-nano.git
cd agentic-ai-nano
# Make changes to public content only
git add . && git commit -m "Update public content"
git push origin main

Content Security Guidelines:

• Corporate Content: Never include BMW-specific details in public content files
• Generic Alternatives: Always provide generic alternatives for corporate-specific instructions
• Conditional Content: Use automatic network detection rather than manual switches
• Images: Corporate images are encrypted and stored separately from public repository

Security Features

• Network-based Access Control: Content visibility based on corporate network detection
• Encryption at Rest: Corporate content encrypted when included in public repository
• Client-side Decryption: Secure decryption only available from corporate network
• IP Range Validation: Multiple corporate IP range patterns for network detection
• Automatic Content Switching: Seamless transition between corporate and public content

This system ensures corporate-specific content remains secure while allowing flexible deployment across both internal and public environments.

Certification

Module Completion: Complete all sessions, achieve 80%+ on assessments, implement practical exercises
Nanodegree Completion: Complete all 3 modules + capstone project integrating all concepts

Ready to build the future of AI agents?

🚀 Start Your Journey: Setup Cloud Workspace →

Transform your commute into productive learning time with our 🎧 Podcast Mode - perfect for hands-free learning while driving!