Agentic AI Professional Program
Master autonomous AI systems, intelligent agents, multi-agent collaboration, and goal-driven decision-making through real-world projects and hands-on labs to build industry-ready Agentic AI solutions that think, plan, and act independently.
Professional Certification
Upon successful completion, you will receive an industry-recognized credential.
Industry-Validated Certification
Accredited by the Global Data Science Alliance and recognized by top tech employers worldwide.
Digital Verification
Includes a unique blockchain-verified ID that can be added directly to your LinkedIn profile.
Completion Criteria
Awarded upon submission of all 5 capstone milestones and passing the final technical assessment.
GOBINATH ARUMUGAM
Chief Technology Officer | AI, ML & Data Science Trainer
With a strong foundation in autonomous systems and advanced AI technologies, Gobinath Arumugam delivers a practical, results-driven approach to Agentic AI training, simplifying complex concepts like intelligent agents, reasoning frameworks, and multi-agent collaboration into clear, structured, and industry-ready skills.
Focused on transforming Agentic AI knowledge into real-world autonomous solutions through hands-on projects, AI agent development, tool integration, planning systems, and real-time decision-making applications.
13+ years
at Xplore IT Corp
150+
Institutions Reached
2000+ Batches
Trained Across India
50,000+
Students Trained
Course Syllabus
A comprehensive 12-week journey from basics to professional mastery.
Week 1 — Python Basics
Theory
• Python installation & environment setup
• Syntax, variables, data types
• Input/Output
• Operators
Lab
• Calculator app
• Temperature converter
Assignment
• CLI L”‘it converter
Week 2 — Control Flow
Theory
• If-else statements
• Loops (for, while)
• Logical operators
• Pattern problems
Lab
• Number guessing game
• Basic ATM simulation
Assignment
• Menu-driven CLI program
Week 3 — Data Structures
Theory
• Lists, tuples
• Dictionaries & sets
• List comprehensions
• Nested structures
Lab
• To-Do ist app
Assignment
• Student record manager
Week 4 — Functions, Files & Exceptions
Theory
• Functions & arguments
• Modules
• File (JSON/CSV)
• Exception handling
Lab
• Expense tracker with file storage
Milestone Project
• CLI Student Management System
Week 5 – OOP in Python
Theory
• Classes
• Inheritance
• Polymorphism
• Magic methods
Lab
• Convert CLI project into OOP system
Assignment
• Library marlagernent system
Week 6 — APIs & Async Programming
 Theory
• REST APIS
• Using requests
• API authentication
• Intro to asyncio
• Virtual env*onments
Lab
• Weather API app
. News fetcher
Mini Project
• API-based dashboard CLI tool
Week 7 – & NLP Basics
Theory
.• Al vs ML vs DL
• NLP fundamentals
• Transformers overview
• Tokens & embeddings (concept)
Lab
• Text preprocessing
• Basic NLP tasks
Assignment
• Text analysis program
Week 8 — Working with LLM APIs
Theory
• API-based LLM usage
• Prompt engineering
• Few-shot prompting
• Role prompting
• Structured JSON outputs
Lab
• Resume analyzer
AI email generator
Assignment
• AI chatbot
Week 9 — Embeddings & Vector Databases
Theory
What are embeddings?
• Semantic similarity
• Vector databases (conceptual overview)
• Introduction to RAG
Lab
• Generate embeddings
• Similarity search system
Assignment
• Semantic FAQ search tool
Week 10 — RAG Architecture
Theory
• Retrieval-Augmented Generation
• Document loaders
• Chunking strategies
• Retriever • Generator flow
Reducing hallucinations
framework Introduction
• LangChain
Lab
• Build document (PDF/Text)
Assignment
• Knowledge base chatbot (CLI)
Week 11 — Advanced RAG
• Hybrid search
• Metadata filtering
• Context window optimization
• Evaluation of RAG systems
Lab
• Multi-document RAG system
ini Project
• RAG-based Research Assistant
Week 12 — Agent Fundamentals
Theory
• What is Agentic Al?
• Tools & function calling
• Memory systems
• Planning basics
Lab
• Tool-using agent (calculator • API)
• Add RAG as tool
Assignment
• Multi-tool assistant agent
Week 13 — Multi-Agent Systems
Theory
• Role-based agents
• Supervisor pattern
• Task delegation
• Guardrails & safety
Framework Intro
• CrewAl
Lab
• Researcher Writer • Editor agent workflow
Mini Project
• Multi-agent blog generator
Week 14 — Productionizing Al Apps
Theory
• FastAPl basics
• REST endpoints
• Middleware
• Streamlit UI
• Docker basics
• Environment variables
Lab
• Deploy RAG agent as API
• Simple web interface
Assignment
• Containerized Al agent app
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Week 15 — Final Project & Presentation
Students must include:
• Python backend
• LLM integration
• RAG pipeline
. At least one tool
• Agent logic
• Deployment
Project Options
1. Autonomous Research Agent
2-AI Financial Analyst
3. Customer Support Al Agent
4. Personal Productivity Agent
5 – Al Code Assistant
Deliverables
• Wtorking demo
• GitHub repository
• Documentation
• Presentation
Final Competency After 15 Weeks
Students will be able to:
Write professional Python applications
• Work with APIs and async workflows
Design and implement RAG systems
• Build tool-using Al agents
• Create multi-agent systems
• Deploy Al applications to production