Industry Skills Classroom Training

Agentic AI Engineer

This course enables you to walk out not just with knowledge — but with the confidence, skills, and real project experience to land an Agentic AI Engineer role. You will be fully prepared to design autonomous agent workflows, build multi-agent systems, integrate tools and memory, implement human-in-the-loop mechanisms, and deploy production-ready agentic AI applications — ready to perform from Day 1 on the job.

5.0 (1 ratings)
4 enrolled 6 months | 4 hours/day Professional Tamil

What You'll Learn

  • Master Python & LLM Fundamentals for Agentic AI Development
  • Build Stateful Agentic Workflows with LangGraph
  • Design & Deploy Multi-Agent Systems with AutoGen
  • Integrate Tools, Memory & RAG into Autonomous Agent Pipelines
  • Build & Monitor Production-Ready Agentic AI Applications
  • Go from Job Seeker to Job-Ready Agentic AI Engineer

Requirements

  • Basic Computer Operating Knowledge
  • No need of Previous Coding Knowledge
  • Suitable for both IT & Non-IT students

Tools & Technologies

Python Python
Ollama Ollama
Hugging Face Hugging Face
Langchain Langchain
LangGraph LangGraph
Tavily Search Tavily Search
ChromaDB ChromaDB
LlamaIndex LlamaIndex
AutoGen AutoGen
Streamlit Streamlit
Fast API Fast API
vLLM vLLM
Phoenix Phoenix
MLflow MLflow
Docker Docker
GitHub GitHub

Course Description

A professionally designed, industry-aligned classroom program built exclusively for job seekers who are serious about becoming an Agentic AI Engineer. This course goes beyond chatbots and simple LLM applications — training you to build autonomous AI systems that can plan, reason, use tools, and complete complex multi-step business tasks with minimal human intervention — giving you the confidence to walk into any AI company and deliver from your very first day at work.

Right for You?

Who This Course is For

  • Fresh Graduates Looking to Start Their Career in Agentic AI & Autonomous Systems
  • Job Seekers Who Want to Build AI That Goes Beyond Simple Chatbots & Assistants
  • Candidates Who Prefer Structured Classroom Learning with Mentor Support
  • Anyone Ready to Commit and Become a Job-Ready Agentic AI Engineer

Course Curriculum

18 modules • 161 lessons
Module 1: Python for Agentic AI 14 lessons
  • Introduction to Python & Environment Setup
  • Python Syntax & Data Types
  • Variables, Operators & Expressions
  • Conditional Statements & Loops
  • Functions & Modules
  • Lists, Tuples, Sets & Dictionaries
  • File Handling & Exception Handling
  • Object Oriented Programming
  • Working with JSON & YAML Files
  • Virtual Environments & Package Management
  • Async Programming & Concurrency for Agent Systems
  • REST API Integration with Python
  • Python Interview Preparation
  • Module 1 Clearance Test
Module 2: LLM Fundamentals for Agentic AI 11 lessons
  • Introduction to Large Language Models
  • Transformer Architecture - Conceptual Overview
  • Open Source LLM Landscape - Llama, Mistral, Phi & Gemma
  • Setting Up & Running Models with Ollama
  • Loading & Running Models from Hugging Face
  • Context Windows, Temperature & Model Parameters
  • Prompt Engineering for Agentic Systems
  • Zero Shot, Few Shot & Chain of Thought Prompting
  • Structured Output Control for Agent Responses
  • Hallucination, Bias & LLM Limitations
  • Module 2 Clearance Test
Module 3: LangChain Foundations 11 lessons
  • Introduction to LangChain & Its Architecture
  • LangChain Components - Models, Prompts & Chains
  • Integrating Ollama & Hugging Face with LangChain
  • Building Simple Chains with LangChain
  • LangChain Expression Language (LCEL)
  • Memory & Conversation History in LangChain
  • LangChain Document Loaders & Text Splitters
  • LangChain Retrievers & Vector Store Integration
  • LangChain Tools & Tool Binding
  • LangChain Output Parsers & Structured Responses
  • Module 3 Clearance Test
Module 4: Introduction to Agentic AI 9 lessons
  • What is Agentic AI & Why It Matters
  • Difference Between Chatbots, Assistants & Agents
  • Agent Components - LLM, Tools, Memory & Planning
  • ReAct Framework - Reasoning & Acting
  • Agent Planning & Task Decomposition
  • Tool Use & Decision Making in Agents
  • Types of Agents - Task, Conversational & Autonomous
  • Real-World Agentic AI Use Cases in Industry
  • Module 4 Clearance Test
Module 5: LangGraph for Agentic Workflows 10 lessons
  • Introduction to LangGraph & Why It Extends LangChain
  • Graph-Based Workflow Concepts - Nodes, Edges & State
  • Building Your First LangGraph Agent
  • State Management in LangGraph
  • Conditional Routing & Branching in Workflows
  • Cycles & Loops in Agentic Workflows
  • Checkpointing & Persistence in LangGraph
  • Building Complex Multi-Step Agent Workflows
  • Debugging & Visualizing LangGraph Workflows
  • Module 5 Clearance Test
Module 6: Tool Use & Function Calling 10 lessons
  • Introduction to Tool Use in Agentic Systems
  • Function Calling with Open Source Models
  • Building Custom Tools for Agents
  • Web Search Tool Integration with Tavily
  • Database Query Tool - PostgreSQL Integration
  • File System & Document Processing Tools
  • REST API Integration as Agent Tools
  • Tool Selection & Dynamic Tool Routing
  • Error Handling & Tool Fallbacks
  • Module 6 Clearance Test
Module 7: Memory Systems for Agents 9 lessons
  • Introduction to Agent Memory Systems
  • Short Term Memory - Conversation Context Management
  • Long Term Memory - Persistent Storage for Agents
  • Episodic Memory - Storing Past Agent Interactions
  • Semantic Memory - Knowledge Base Integration
  • Vector Store Based Memory with ChromaDB
  • Memory Retrieval & Relevance Scoring
  • Building Agents with Persistent Memory
  • Module 7 Clearance Test
Module 8: Multi-Agent Systems with AutoGen 11 lessons
  • Introduction to Multi-Agent Systems
  • Why Multi-Agent - Benefits & Use Cases
  • Introduction to AutoGen by Microsoft
  • Setting Up AutoGen with Ollama
  • Defining Agent Roles & Personas
  • Agent Communication & Message Passing
  • Orchestrator & Executor Agent Patterns
  • Building a Collaborative Multi-Agent Pipeline
  • Group Chat & Agent Debate Patterns
  • Handling Conflicts & Consensus in Multi-Agent Systems
  • Module 8 Clearance Test
Module 9: RAG for Agentic Systems 8 lessons
  • Introduction to RAG in Agentic Context
  • Vector Databases for Agent Knowledge - ChromaDB & FAISS
  • Building a Knowledge Retrieval Tool for Agents
  • Dynamic RAG - Agents Deciding When to Retrieve
  • Multi-Source RAG for Agents
  • Agentic RAG with LlamaIndex
  • Combining RAG with Multi-Agent Workflows
  • Module 9 Clearance Test
Module 10: Human in the Loop Systems 8 lessons
  • Introduction to Human in the Loop - Why It Matters
  • Approval Workflows - Agent Pausing for Human Input
  • Implementing Interrupts & Checkpoints in LangGraph
  • Human Feedback Integration in Agent Pipelines
  • Building Review & Approval Mechanisms
  • Escalation Patterns - When Agents Should Stop
  • Balancing Autonomy & Human Control
  • Module 10 Clearance Test
Module 11: Agentic Workflow Automation 9 lessons
  • Introduction to Agentic Workflow Automation
  • Task Planning & Goal Decomposition
  • Sequential vs Parallel Task Execution
  • Building an Autonomous Research Agent
  • Building an Autonomous Data Analysis Agent
  • Building an Autonomous Coding Agent
  • Building an Autonomous Customer Support Agent
  • Error Recovery & Self-Healing in Agent Workflows
  • Module 11 Clearance Test
Module 12: Agentic AI for Real Business Use Cases 9 lessons
  • Agentic AI in Customer Support Automation
  • Agentic AI for Research & Summarization
  • Agentic AI for Data Analysis & Reporting
  • Agentic AI for Document Processing & Extraction
  • Agentic AI for Code Generation & Review
  • Agentic AI for HR & Recruitment Workflows
  • Agentic AI for Sales & Lead Generation
  • End-to-End Business Case Study
  • Module 12 Clearance Test
Module 13: Building Agentic Apps with Streamlit & FastAPI 8 lessons
  • Introduction to Agentic App Development
  • Building an Agent Chat Interface with Streamlit
  • Displaying Agent Reasoning & Tool Use in Streamlit
  • Building a Multi-Agent Dashboard with Streamlit
  • Building a REST API for Agent Systems with FastAPI
  • Streaming Agent Responses via FastAPI
  • Deploying Agentic Apps with Streamlit & Docker
  • Module 13 Clearance Test
Module 14: Model Serving & Optimization 7 lessons
  • Introduction to Model Serving for Agentic Systems
  • Running LLMs Locally with Ollama for Agents
  • Introduction to vLLM for High Performance Serving
  • Setting Up & Running Models with vLLM
  • Optimizing LLM Inference for Agent Latency
  • Containerizing Agent Systems with Docker
  • Module 14 Clearance Test
Module 15: Agent Evaluation & Safety 8 lessons
  • Introduction to Agent Evaluation
  • Evaluating Agent Task Completion & Accuracy
  • Evaluating Tool Use & Decision Quality
  • Hallucination Detection in Agent Outputs
  • Guardrails & Output Filtering for Agents
  • Preventing Prompt Injection in Agentic Systems
  • Responsible AI Practices for Agentic Systems
  • Module 15 Clearance Test
Module 16: Agent Observability & Monitoring 9 lessons
  • Introduction to Agent Observability
  • Tracing Agent Runs & Tool Calls
  • Introduction to Phoenix by Arize for Agent Tracing
  • Debugging Agent Pipelines with Phoenix
  • Tracking Agent Performance & Token Usage
  • Introduction to MLflow for Agent Experiment Tracking
  • Logging & Comparing Agent Runs with MLflow
  • Production Monitoring Best Practices for Agentic Systems
  • Module 16 Clearance Test
Module 17: Version Control & Agentic Project Structure 8 lessons
  • Introduction to Git & GitHub
  • Git Basics - Clone, Commit, Push & Pull
  • Branching, Merging & Pull Requests
  • Agentic AI Project Folder Structure Best Practices
  • Managing API Keys & Secrets Safely with .env
  • Writing README & Documenting Agentic AI Projects
  • Building a GitHub Portfolio for Agentic AI
  • Module 17 Clearance Test
Module 18: Projects 2 lessons
  • Mentored Project
  • Capstone Project
Our Advantage

Why Choose This Course

  • Built Exclusively for Job Seekers
  • Only Course Covering LangGraph & AutoGen in a Classroom
  • Classroom Training with Real Mentor Guidance
  • Build Autonomous AI Systems That Impress Any Recruiter

Your Instructor

Parthiban Kannan

Parthiban Kannan

Co-founder & Manager

17 Courses

14+ years of experience in digital transformation, leading teams of 15+ members and delivering 160+ projects for organizations like Lakshmi Machine Works, Milacron, Schneider Electric, Aatomz Research, and Variablz Technologies. AI innovator with a patent approved by the Government of India, with strong expertise in Data Science, Data Analysis, Business Intelligence, and Software Development.

FAQ

Frequently Asked Questions

Is this course suitable for freshers with no AI experience?

Yes. This course starts from Python and LLM fundamentals and gradually takes you through the complete Agentic AI stack u2014 no prior AI or agent experience is required to join.

What are the eligibility criteria to join this course?

Any graduate from any degree is eligible, provided their degree includes Mathematics as a subject.

Is this course suitable for college students or working professionals?

No. This course is exclusively designed for job seekers who are seriously looking to start their career as an Agentic AI Engineer.

Is this an online or offline course?

This is a completely classroom-based training program conducted at our Cuddalore location.

Will I work on real projects during this course?

Yes. You will work on a Mentored Project with trainer guidance and an independent Capstone Project u2014 both built around real-world agentic AI scenarios including autonomous workflow automation and multi-agent systems.

What is the difference between this course and the LLM Developer course?

The LLM Developer course focuses on building text-based LLM applications like chatbots and RAG systems. The Agentic AI Engineer course goes deeper u2014 building autonomous systems that can plan, reason, use tools, coordinate multiple agents, and complete complex business tasks with minimal human intervention.

Do I need any prior knowledge of LangChain before joining?

No. LangChain is covered from scratch in Module 3 u2014 you will be guided step by step before moving into advanced agentic frameworks like LangGraph and AutoGen.

Are all tools used in this course free?

Yes. Every tool used in this course u2014 Python, Ollama, LangChain, LangGraph, AutoGen, ChromaDB, FAISS, Streamlit, FastAPI, vLLM, Phoenix by Arize, MLflow, Docker, and GitHub u2014 is completely free and open source.

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