Agentic AI Nano-Degree¶

Welcome to the comprehensive Agentic AI Nano-Degree program! This intensive curriculum covers the complete landscape of modern AI agent development and Retrieval-Augmented Generation (RAG) systems.

Program Overview¶

This nano-degree consists of three comprehensive modules designed to take you from fundamentals to production-ready implementations:

Module 1: Agent Frameworks (10 Sessions)¶

Master the art of building intelligent agents using cutting-edge frameworks and patterns.

What You'll Learn: - Bare metal agent implementation patterns - LangChain and LangGraph workflow orchestration - CrewAI team-based agent coordination - PydanticAI type-safe agent development - Atomic Agents modular architecture - ADK (Agent Development Kit) integration - Agno production-ready deployment - Multi-agent coordination patterns - Enterprise integration strategies

Module 2: RAG Architecture (10 Sessions)¶

Build sophisticated Retrieval-Augmented Generation systems for enterprise applications.

What You'll Learn: - RAG architecture fundamentals and evolution - Advanced chunking and preprocessing strategies - Vector database optimization techniques - Query enhancement and context augmentation - RAG evaluation and quality assessment - Graph-based RAG implementations - Agentic RAG systems - Multi-modal RAG architectures - Production deployment and enterprise integration

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

Master the Model Context Protocol, Agent Communication Protocol, and sophisticated agent-to-agent communication patterns.

What You'll Learn: - MCP (Model Context Protocol) fundamentals and server development - Secure MCP implementations with authentication and authorization - ACP (Agent Communication Protocol) architecture and patterns - Agent-to-agent communication strategies and coordination - Advanced agent workflow orchestration - Production deployment of agent communication systems - Security and compliance in multi-agent environments - Enterprise integration of communication protocols - Scalable agent coordination architectures

Learning Paths¶

Each session offers three distinct learning paths to accommodate different learning styles and time commitments:

👀 Observer Path (30-50 min)¶

• Focus: Conceptual understanding
• Activities: Read core concepts, examine patterns
• Ideal for: Quick overview, decision makers, time-constrained learners

🙋‍♂️ Participant Path (60-90 min)¶

• Focus: Guided implementation
• Activities: Follow exercises, analyze examples
• Ideal for: Active learners, developers, technical managers

🛠️ Implementer Path (120-180 min)¶

• Focus: Hands-on development
• Activities: Build systems, explore advanced patterns
• Ideal for: Engineers, architects, deep technical expertise

Prerequisites¶

Technical Background: - Intermediate Python programming experience - Basic understanding of APIs and web services - Familiarity with Git and command-line tools - Understanding of basic machine learning concepts

Recommended Knowledge: - Experience with REST APIs - Basic knowledge of databases - Understanding of cloud computing concepts - Familiarity with containerization (Docker)

Learning Outcomes¶

Upon completion of this nano-degree, you will be able to:

Agent Development: - Design and implement sophisticated AI agents using multiple frameworks - Create multi-agent systems with proper coordination and communication - Deploy production-ready agents with monitoring and scaling capabilities - Integrate agents into enterprise workflows and systems

RAG Systems: - Architect and build comprehensive RAG systems from scratch - Implement advanced retrieval strategies and query optimization - Evaluate and improve RAG system performance and accuracy - Deploy enterprise-grade RAG solutions with proper security and compliance

Agent Communication & Protocols: - Master MCP and ACP protocol implementations - Build secure, scalable agent-to-agent communication systems - Design sophisticated multi-agent coordination patterns - Deploy enterprise-grade agent communication infrastructures

Tools & Technologies¶

Frameworks & Libraries: - LangChain & LangGraph - CrewAI - PydanticAI - Atomic Agents - ADK (Agent Development Kit) - Agno

RAG Technologies: - Vector databases (Chroma, Pinecone, Weaviate) - Embedding models (OpenAI, Sentence Transformers) - LLMs (GPT-4, Claude, Llama) - Graph databases (Neo4j) - Search engines (Elasticsearch)

Infrastructure: - Docker & Kubernetes - GitHub Actions - Cloud platforms (AWS, GCP, Azure) - Monitoring tools (Prometheus, Grafana)

Course Structure¶

Each session follows a consistent structure:

1. Learning Navigation Hub - Choose your path and time investment
2. Core Content - Essential concepts and implementations (required)
3. Optional Modules - Deep-dive topics for advanced learners
4. Hands-on Exercises - Practical implementation experience
5. Assessment - Multiple choice tests with detailed solutions
6. Navigation - Clear paths to previous/next sessions

Quick Start¶

Ready to begin your journey? Choose your starting point:

Study Tips¶

For Best Results: - Complete sessions in order within each module - Practice with the provided code examples - Implement the exercises in your own environment - Join the community discussions and share your progress - Take breaks between intensive sessions

Time Management: - Observer Path: 1-2 sessions per day - Participant Path: 1 session per day - Implementer Path: 1 session every 2-3 days

Community & Support¶

• GitHub Discussions: Ask questions and share insights
• Code Examples: All implementations available in the repository
• Regular Updates: Course content updated with latest best practices
• Community Contributions: Welcome improvements and additions

Ready to become an expert in Agentic AI? Choose your path and start your journey today!

Get Started with Agent Frameworks → Explore RAG Architecture → Master Agent Communication →