Agentic AI & Model Context Protocol (MCP) Engineering

Module 1: Introduction to Agentic AI 

Conceptual Foundations

• Evolution of AI systems: Traditional AI → LLMs → Agentic AI
  
• Why autonomous agents represent the next paradigm shift
  
• From prompt-response systems to goal-driven execution
  

Enterprise Use Cases

• Research automation bots
  
• AI-powered customer service assistants
  
• Marketing and content automation agents
  
• Knowledge retrieval assistants
  

Agent Architecture Overview

• Single-agent systems
  
• Multi-agent systems (high-level introduction)
  
• Orchestration and coordination concepts
  

Live Demonstration

• Execute a ready-made automation workflow using n8n
  
• Breakdown of trigger → reasoning → tool usage → output
  

Module 2: Core Agent Concepts Simplified

Core Components of an AI Agent

• Memory – Context retention and state management
  
• Planning – Task decomposition and execution sequencing
  
• Tool Usage – API calls, database queries, web browsing
  
• Autonomy – Decision-making without constant human prompts
  

Agent Lifecycle

• Input → Reason → Act → Observe → Iterate
  

Practical Discussion

• Where agents fail
  
• Limitations of autonomy
• When to use agents vs. simple automation

Module 3: Hands-On with n8n – No-Code Agent Development

Platform Foundations

• n8n interface walkthrough
  
• Workflow building fundamentals
  
• Node configuration and data passing
  
• Credentials and integrations
  

Agent Design Patterns

• Trigger → Action → Output
  
• Decision branching
  
• Error handling and retries
  
• External API integrations
  

Agent Build Activities

Participants will build:

1. Content Creation Agent
2. Onboarding Automation Agent
3. Tool-Integrated Agent (API-based)
4. Agent with MCP integration
   

Extensions

• Push outputs to Slack or Telegram
  
• Logging and monitoring workflows
  

Hands-On Lab

Participants design and deploy their own workflow-based agent from scratch.

Module 4: Hands-On with ChatGPT Custom GPTs

Custom GPT Foundations

• Writing effective system instructions
  
• Adding knowledge files
  
• Configuring actions (external tools)
  

Use Case Build

• Build a Custom HR Assistant GPT:
  
  • FAQs automation
    
  • Onboarding email drafts
    
  • Interview preparation guidance
    

Practical Lab

Each participant builds a domain-specific Custom GPT aligned to their industry.

Module 5: Agent Frameworks & Multi-Agent Systems

LangChain & ReAct Framework

• Understanding ReAct (Reason + Act) pattern
  
• Building agents using LangChain
  
• Tool calling and chaining
  

Multi-Agent Architectures

• Role-based agent systems
  
• Agents for:
  
  • Web search
    
  • SQL querying
    
  • Analytical reasoning
    
• Coordination and shared memory
  

CrewAI Framework

• Introduction to CrewAI
  
• Defining roles:
  
  • Researcher
    
  • Analyst
    
  • Writer
    
• Task delegation and orchestration
  

Framework Comparison

• CrewAI vs. LangChain Agents
  
• Architecture structure
  
• Simplicity vs. flexibility
  
• Orchestration trade-offs
  

Hands-On Labs

• Build a single autonomous agent
  
• Develop a multi-agent system
  
• Implement agents using:
  
  • JSON configurations
    
  • Python-based implementations

Module 6: Model Context Protocol (MCP) – Architecture & Implementation

MCP Fundamentals

• What MCP is
  
• The interoperability challenges it solves
  
• Standardizing tool and context exchange
  

MCP Architecture

• Client ↔ Server interaction model
  
• Transport mechanisms
  
• Context injection patterns
  

Core MCP Features

• Tools
  
• Resources
  
• Prompts
  
• Transport protocols
  
• Streamable HTTPS
  

MCP Development

• Building MCP servers from scratch
  
• Building MCP clients
  
• Real-world implementation patterns
  

Integration with Agent Systems

• Connecting MCP to LLM frameworks
  
• Integrating with agentic architectures
  
• Cross-platform tool interoperability
  

Packaging & Deployment

• Packaging MCP services
  
• Publishing and hosting
  
• Deployment considerations
  

MCP with Python

• Building MCP components using Python
  
• Integrating MCP into Python-based agent systems
  
• Testing and debugging workflows