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Mastering AI Agent
Mastering AI Agent
Curriculum
15 Sections
75 Lessons
Lifetime
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Module 01 - Foundation of Agentic AI
In this Module we are covering, Introduction to AI Agents, Core Concepts (Autonomy, Reactivity, and Adaptability), Real-World Applications, Difference Between AI Agents surpass Traditional RPA.
5
1.1
Introduction to AI Agents
1.2
Core Concepts – Autonomy, Reactivity, and Adapatability.
1.3
Real-World Applications
1.4
Difference Between RPA and AI Agents
1.5
Quiz – Module 01
3 Questions
Module 02 - Programming Basics
In this Module, We will Learn Python Programming needed to built AI Agent
8
2.1
Python Fundamentals
2.2
Quiz – Python
0 Questions
2.3
Object-Oriented Programming (OOPs)
2.4
Asynchronous Programming for Multi-Tasking Agents
2.5
APIs as Agent Gateways
2.6
Connecting OOPs + Async + APIs
2.7
APIs & Data Handling (REST, WebSockets, JSON)
2.8
Logic & Algorithm Basics for Decision-Making
Module 03 - Understanding LLMs as the Brain of Agents
In this Module, we will learn Large Language Models (LLMs) and mechanism of LLMs
5
3.1
Transformer Architecture
3.2
Prompting Techniques
3.3
Function Calling & Embedding
3.4
Parameter-Efficient, Fine-Tunning (LoRA, PEFT)
3.5
Working with APIs: OpenAI, Anthropic, Hugging Face
Module 04 - Building Converaation Agent
In this Module, We are going to build a Conversational Agent
4
4.1
LLM Basics Recap
4.2
Prompt Engineering Deep Dive
4.3
Tools Fundamentals (LangChain, CrewAI, AutoGPT)
4.4
Knowledge Integration (Docs, APIs, Databases)
Module 05 - Agent Architecture & Frameworks
We will learn Architecture and Frameworks of AI Agents.
6
5.1
Overview of Agentic AI Frameworks
5.2
Agentic vs Non-Agentic System
5.3
Types of Agentic Architecture
5.4
Overview of AI Agent Frameworks
5.5
Types of AI Agent Frameworks
5.6
Deep Dive into Agentic AI Frameworks
Module 06 - Planning, Reflection, and Evaluation
In this Module, we will see the Planning, Reflection and Evaluation of AI Agent syste,m.
5
6.1
Introduction to Agent Improvement
6.2
Planning & Reasoning Techniques
6.3
Reflection & Criticism
6.4
Testing & Evaluation
6.5
Monitoring & Continuous Improvement
Module 07 - Multi-Agent Systems
In this Module, We will learn Multi-Agent Systems.
8
7.1
Introduction to Multi-Agent Systems
7.2
Core concepts & Definitions in Multi-Agent Systems
7.3
Why Multi-Agent Systems matter
7.4
Architectural Patterns of Multi-Agent System
7.5
Agent Characteristics
7.6
Communication and Coordination Mechanisms
7.7
State & Data Management
7.8
Learning & Adaption in Multi-Agent System
Module 08 - AI Agent Protocols
In this Module, We will learn how multi-agents communicate.
7
8.1
Introduction to Protocols
8.2
Model Context Protocols – MCP
8.3
Agent 2 Agent Protocol – A2A
8.4
Agent Communication Protocol – ACP
8.5
Agent Network Protocol – ANP
8.6
AGORA
8.7
Comparing AI Agent Protocols
Module 09 - Model Context Protocol - MCP
In this module, we will learn one of the best Protocol, it's MCP
5
9.1
Introduction & What is MCP?
9.2
Core Purpose and Benefits
9.3
MCP Architecture
9.4
Roles, Scopes, and State Preservation
9.5
Real-World use Case
Module 10 - Reinforcement Learning Agents
In this Module, We will learn how the Agent Learn from Environment using Machine Learning Algorithm names Reinforcement Learning (RL).
5
10.1
RL Foundation
10.2
Core Algorithm (Q-Learning, PPO, DQN)
10.3
RL in Agent Environment
10.4
Tools & Framework
10.5
Applications & Challenges.
Module 11 - Guardrails, Ethics & Compliance
0
Module 12 - Deploying and Integrating AI Agents
In this Module, We Will learn how to Deploy a AI Agent.
4
12.1
Web Apps and API Basics
12.2
Deployment Options
12.3
Scaling
12.4
Monitoring & Maintance.
Module 13 - AI Agent Evaluation Frameworks
Deployment Options
8
13.1
Introduction to AI Agent Evaluation Framework
13.2
Framework Overview
13.3
LangSmith
13.4
Google ADK Eval
13.5
Arize Phoenix
13.6
AWS Labs Agent Eval
13.7
Langfuse
13.8
Pydantic Evals
Module 14 - Agentic SDLC & Human–AI Collaboration
7
14.1
AI Agent Progression Framework (Level 1 → Level 5)
14.2
Introduction to Agentic SDLC
14.3
Mapping SDLC Phases to Human–AI Roles
14.4
6 Stages of AI Agent Development (adapted from this infographic)
14.5
Multi-Agent Roles in SDLC
14.6
Comparing with Traditional SDLC
14.7
Case Study: End-to-End AI Agent Development
Module 15 - Capstone Projects
0
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