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Welcome to the Agentic AI module. Step beyond static prompts and sequential pipelines to build autonomous systems that use reasoning loops, invoke external tools, and collaborate to achieve complex objectives.

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💻 Module Practice Notebooks

Master all the concepts from this module with hands-on practice (excluding MCP):
  • Practice in VS Code: Open the notebook in your local editor. Requires a local .env file containing your API keys.
  • Practice in Google Colab: Open the notebook directly in Colab. Setup cells are included to install packages and request API keys.
💻 VS Code | 🚀 Colab | 📥 Download Notebook

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🗺️ Module Overview

Transition from static pipelines to fully autonomous agents:

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📚 Module Curriculum & Roadmap

Explore the sections in this module using the cards below:

1. Introduction to Agents

Compare static chains and autonomous agents, explore planning and memory systems, and study the ReAct reasoning-action loop pattern.

2. Tool Use & Function Calling

Understand the API function calling payload contract, declare tools using the @tool decorator, and bind tools to chat models.

3. Building a ReAct Agent

Construct a complete working ReAct agent using LangGraph. Connect calculation tools and data directory tools to solve complex queries.

4. Multi-Agent Orchestration

Study multi-agent communication patterns, separation of concerns, and analyze CrewAI, LangGraph, and AutoGen orchestration frameworks.

5. Stateful Multi-Agent Application

Build a custom, stateful Resume Analyzer & Interview Planner pipeline using LangGraph nodes, shared states, custom keyword checking tools, and a MemorySaver checkpointer.

6. Stateful Travel Assistant Project

Construct a stateful Travel Assistant (Weather Advisory & Budget Planner) graph manually. Build custom nodes, bind search tools, and compile with persistent thread memory.

7. Model Context Protocol (MCP)

Understand the requirement for MCP, its architecture (Host, Client, Server), and connect to the official GitHub MCP server to query repo details in Python.

8. Creating a Custom MCP Server

Build a custom FastMCP Python Math Server exposing calculator tools, connect a stdio transport subprocess, and bind them dynamically to a Gemini Chat Agent.

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🛠️ Practical Capstone Project

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Autonomous Customer Support Agent

Build a multi-tool agent that handles user tickets:
  1. Tool 1: DB Lookup: Queries database to fetch user account status and order details.
  2. Tool 2: KB Search: A mini RAG system to search company policies/knowledge base.
  3. Tool 3: Email Sender: Mock-sends an email update to the user.
  4. The Agent: Receives a complaint (e.g., “Where is order #1002?”), reasons about which tool to call, fetches details, checks shipping status policies, sends a resolution email, and answers the customer.