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Contents of Agentic AI

Below you can see the sample video

The Complete 2025 Agentic AI Bootcamp

Become an Agentic AI expert in ONE course. Python, RLlib, OpenAI Gym, AutoGPT, Q-Learning, LangChain, and GPT fine-tuning.

Created By: Praveen

Duration: 12 Weeks

The actual course materials are available here.

Pre-Requisites are mentioned below

  • Programming Skills - Python programming with focus on image processing and numerical computing

  • Mathematics - Basic understanding of linear algebra, calculus, and statistics

  • Machine Learning Fundamentals - Basic understanding of machine learning concepts and neural networks

  • Image Processing Tools - Familiarity with OpenCV, PIL, or similar Agentic AI libraries is helpful

Course Content

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Module 1: Foundations of Agentic AI (Week 1-2)

  • Introduction to Agentic AI & Autonomous Systems

  • What makes AI agentic? (Perception, Reasoning, Decision-Making, Acting)

  • Comparison: Reactive AI vs. Agentic AI

  • Markov Decision Processes (MDPs)

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Module 2: Reinforcement Learning for AI Agents (Week 3-4)

  • Basics of Reinforcement Learning (RL)

  • Q-Learning & Deep Q Networks (DQN)

  • Introduction to Gym & RLlib for AI environments

  • Policy-based vs. Value-based learning

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Module 3: Large Language Models (LLMs) for AI Agents (Week 5-6)

  • How LLMs reason & make decisions

  • ReAct: Reasoning + Acting in Language Models

  • Using LangChain for AI Agent developmen

  • Fine-tuning GPT-based agents

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Module 4: Planning & Multi-Agent Systems (Week 7-8)

  • AI Planning: A* Search, Decision Trees

  • Multi-Agent Systems: Agent collaboration & competition

  • Game Theory for AI Agents

  • Implementing self-learning multi-agent AI

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Module 5: Tool-Using AI Agents (Week 9-10)

  • Tool Use & API Integration in AI Agents

  • AutoGPT & BabyAGI: Autonomous Task Execution

  • Combining LLMs with APIs (e.g., Web search, APIs, databases)

  • Memory & Learning for AI agents

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Module 6: Advanced Agentic AI & Real-World Applications (Week 11-12)

  • Autonomous AI agents in the real world (Trading bots, AI assistants, Robotics)

  • Combining LLMs + Reinforcement Learning for self-improving AI

  • Ethics & Security in Agentic AI

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Hands On from all the Modules

  • Implement a basic rule-based AI agent in Python

  • Simulate a simple autonomous agent in a grid environment

  • Train an AI agent using Q-learning in OpenAI Gym

  • Implement DQN for a simple game (CartPole, LunarLander)

  • Implement an AI assistant using OpenAI API

  • Use LangChain to create an LLM-driven agent

  • Implement an AI planner for navigation

  • Create a multi-agent simulation with simple bots

  • Build a self-learning AI agent

  • Create an AI agent that interacts with APIs (e.g., retrieves stock data, writes emails)

  • Implement an AI assistant using OpenAI API

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Hands-On (Final Project):

  • Project 1: AI-powered Research Assistant (AutoGPT-based)

  • Project 2: AI Trading Bot using Reinforcement Learning

  • Project 3: AI Agent for Automated Content Generation

Description

This Agentic AI course offers a focused exploration of intelligent systems capable of perception, reasoning, and autonomous action. Beginning with foundational concepts like Markov Decision Processes and reactive vs. agentic architectures, you'll progress to implementing reinforcement learning agents using Gym and RLlib. The curriculum covers LLM-powered systems through LangChain and GPT fine-tuning, while exploring multi-agent collaboration and game theory applications. Hands-on projects include building API-integrated autonomous agents, self-learning systems, and practical implementations of AutoGPT/BabyAGI concepts. You'll emerge ready to design AI systems that perceive environments, make strategic decisions, and execute complex tasks autonomously.

Course Videos

Instructor

Instructor Image

Praveen

Email: praveen.manupati@isanghanminds.com

Phone: +91

(4.5)

praveen is an experienced software engineer with over 10 years of experience in the industry. He specializes in web development and has a passion for teaching others.