Built by 2 times CII Award winning industry veteran

Master Agentic AI

  • Don't graduate with just theory. Build 12+ real AI agents that recruiters can see on your GitHub.
  • Stand out or get filtered out. Get a Verifiable Certificate + Live Project opportunity.
  • Compete and win. Exclusive access to a nationwide AI competition with cash prizes — only for AgentVidya students.
₹24,999 ₹9,999 Early Bird — 60% off
7-day money-back guarantee. Zero risk.
32 weeks of project-based learning. Self-paced. Online.
WHAT YOU'LL LEARN • HANDS-ON CURRICULUM
What You'll Build

AI Research
Agent

Build an autonomous agent that searches the web, reads papers, and generates structured reports.

What You'll Build

Multi-Agent
System

Design a crew of specialized AI agents that collaborate, debate, and solve complex tasks together.

What You'll Build

RAG
Pipeline

Build a complete retrieval-augmented generation system with vector DBs, chunking, and knowledge graphs.

What You'll Build

Autonomous
Coder

Build an agent that writes, tests, and debugs its own code — the kind top AI labs are building.

What You'll Build

Customer
Support Bot

Create a production-ready support agent with persistent memory, tool calling, and real-time retrieval.

What You'll Build

Tool Use
& MCP

Integrate multiple tools with error handling. Build and register a custom MCP server.

What You'll Build

Agent
Guardrails

Implement safety guardrails, red-teaming, and prompt injection defense for production agents.

What You'll Build

Pipeline
Optimization

Cost Optimization, Failure Analysis, Self-healing.

Tools covered
Python LangChain ChromaDB DSPy Pydantic & many more..

Why engineers choose AgentVidya

Our curriculum is designed by CII award winning industry veteran — who've shipped real products.

Follows Industry standards
See the curriculum

40 labs + Capstone Projects. Your GitHub profile becomes your resume — recruiters can see exactly what you've built.

Job ready Developer Portfolio

Get a verifiable certificate that employers can actually check. Plus exclusive access to a nationwide AI competition with cash prizes.

Verifiable Certificate

Live Project opportunity with real deliverables. Work on specialization-specific AI projects that go on your resume and GitHub.

Live Project will kickstart post course completion
01.

Quality Instruction

Clear explanations of complex AI concepts. No jargon without explanation. Learn from experts.

02.

Hands-On Projects

12+ guided projects from simple chatbots to autonomous multi-agent systems. Every concept is taught hands-on.

03.

Career Outcomes

Verifiable certificate, Live project, Developer portfolio. Everything you need to stand out.

04.

Interactive Learning

Not just video lectures. Interactive coding challenges, real-time feedback to maximize your learning.

AgentVidya

Course
Structure

01
PART 1 / WEEKS 1-12

Foundations

Introduction to LLMs & Agent Fundamentals
Tool Use, MCP & Function Calling
Reasoning, Planning, Task Decomposition Patterns
RAG, Knowledge Graphs & Memory
Frameworks: LangChain, CrewAI, AutoGen
Learning & Adaptation
02
PART 2 / WEEKS 13-24

Advanced

Multi-Agent Systems & Swarms
Human-Agent Collaboration & Trust
Evaluation, Observability & A/B Testing
Safety, Guardrails & Red-Teaming
Cost Optimization, Failure Analysis & Self-healing
Domain Specific Agents
03
PART 3 / WEEKS 25-32

Outcomes

4 Internal evaluations (50%)
Lab Portfolio — 40 lab sessions (25%)
Guided capstone projects (25%) & Github Profile Development
Live Project
National Competition

Your 8-Month Learning Journey

Month 1-2: Foundations I

AI agent fundamentals, LLM introductions, reasoning & planning

Weeks 1-6
P
M1
M2
M3

Month 2-3: Foundations II

Function calling, MCP, tool composition & chaining, Memory, RAG, knowledge graphs, context management, fine-tuning

Weeks 7-12
M4
M5
M6
M7

Month 4-5: Advanced I

Multi-Agent Systems, human-agent collaboration, trust calibration, evaluation suites, guardrails

Weeks 13-18
M8
M9
M10
M11

Month 5-6: Advanced II

Optimization, domain specific agents, industry applications, capstone projects

Weeks 19-24
M12
M13
M14

Month 7: Live Project

Work on a real life business problem and develop a solution

Weeks 25-28
LP

Month 8: National Competition

Nationwide competition amongst AgentVidya students

Weeks 29-32
NC
Month 1
Month 2
Month 3
Month 4
Month 5
Month 6
Month 7
Month 8
W1W2W3W4 W5W6W7W8 W9W10W11W12 W13W14W15W16 W17W18W19W20 W21W22W23W24 W25W26W27W28 W29W30W31W32

What you'll build
in 8 months

AgentVidya
14 Modules · 63 Lectures · 40 Labs · 4 Internal exams · Guided capstone projects
Module
What You'll Go Through
Key Thought
Outcomes
Module 1
Weeks 1-2
Introduction to AI Agents
3L Agent fundamentals, autonomy spectrum, agent taxonomy, history of AI agents

What is an agent vs chatbot vs traditional software? When to use agents — and when NOT to.

2 Lab Build your first agent + agent persona design

LLM API + system prompts + conversation loop. Design 3 agent personas and test them.

"An agent is not a chatbot. It reasons, plans, and acts."
Working agent with custom persona.
Module 2
Weeks 3-5
LLM Foundations for Agents
5L Transformers, tokenization, prompt engineering, CoT, multimodal, prompt optimization, prompt chaining

Deep dive into LLM internals. System prompts, few-shot, structured output, prompt injection defense, production prompt management.

3 Lab Prompt engineering + multimodal agent + prompt optimization

Structured output, tool call generation, A/B testing prompts for cost vs quality trade-offs.

"Understanding how LLMs think is the foundation of building agents that reason."
🎯
Optimized prompt system + multimodal agent.
Module 3
Weeks 6-7
Agent Reasoning & Planning
7L CoT, Tree-of-Thought, ReAct, Plan-Execute, reflection, task decomposition, error recovery, thinking budgets

How agents think, plan, and recover from failures. Goal setting with token budget allocation.

4 Lab ReAct agent + Plan-Execute vs ReAct comparative

Build both architectures. Ablation study: disable each component and measure impact.

"The best agents don't just react — they plan, reflect, and self-correct."
ReAct + Plan-Execute agents. Internal Exam 1.
Module 4
Weeks 8-9
Tool Use & Function Calling
4L Function calling, tool schema design, dynamic tool selection, MCP, code execution & sandboxing

OpenAI vs Anthropic APIs. Tool composition, chaining, and the Model Context Protocol.

2 Lab Multi-tool agent + MCP server implementation

Integrate tools with error handling. Build and register a custom MCP server.

"Tools turn a language model from a talker into a doer."
⚙️
Multi-tool agent + custom MCP server.
Module 5
Weeks 10-12
Agent Memory & Knowledge
5L Memory architectures, vector DBs, RAG, chunking, retrieval, advanced RAG, knowledge graphs, context management

ChromaDB, FAISS, Graph RAG vs vector RAG. Memory consolidation and eviction strategies.

3 Lab Complete RAG pipeline + knowledge graph agent + agentic RAG

Ingest→chunk→embed→retrieve→generate. Self-querying + multi-hop + corrective retrieval.

"An agent without memory is a goldfish. An agent with RAG is a librarian."
📝
RAG pipeline + knowledge graph agent. Mini-project due.
Module 6
Weeks 13-14
Implementation Frameworks
5L LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, framework selection guide

Master the industry's most-used agent frameworks. Learn when to use which — and why.

2 Lab LangGraph agent + framework shootout (same task in 3 frameworks)

Implement the same task in LangGraph + CrewAI + AutoGen. Write a comparison report.

"The best framework is the one that fits your problem — not the one with the most stars on GitHub."
👥
3 framework implementations + comparison report.
Module 7
Weeks 15-16
Learning & Adaptation
3L Fine-tuning (LoRA, QLoRA), RLHF, DPO, self-improving agents

When to fine-tune vs RAG vs prompt engineer. Preference alignment and constitutional AI.

2 Lab LoRA fine-tuning + self-improving agent

Fine-tune for agent-specific behavior. Build a reflection loop with iterative refinement.

"Fine-tune for a specialist. RAG for a researcher. Prompt for a generalist."
Fine-tuned agent. Internal Exam 2. Synthesis Week 1.
Module 8
Weeks 17-19
Multi-Agent Systems
5L Orchestrator-worker, peer-to-peer, communication protocols, swarms

How agents collaborate, compete, and produce emergent behavior. Debugging multi-agent deadlocks.

3 Lab Orchestrator-worker + peer-to-peer + agent swarm

Build a 3-agent coordinator. Direct communication without central control. Emergent swarm behavior.

"One agent is smart. A team of agents is powerful — if they can communicate."
🎯
Multi-agent system with 3 architectures.
Module 9
Weeks 20-21
Human-Agent Collaboration
6L HITL patterns, transparency, trust calibration, ethics of collaboration

When should humans be in the loop? Design agents people trust — without over-trusting.

2 Lab HITL agent + transparent reasoning visualization

Approval gates + escalation logic. Visualize agent reasoning so non-coders can understand.

"The best agents know when to ask for help."
HITL agent + transparent reasoning UI.
Module 10
Weeks 22-23
Evaluation & Testing
6L Metrics, LLM-as-judge, benchmarks, observability (LangSmith), A/B testing

How to measure if your agent actually works. AgentBench, SWE-bench, tracing, eval-driven development.

2 Lab Evaluation suite + observability setup

Build multi-metric benchmarks. Trace, debug, and identify failure patterns.

"If you can't measure it, you can't improve it."
⚙️
Evaluation suite + observability dashboard. Internal Exam 3.
Module 11
Weeks 24-25
Safety, Security & Governance
6L Guardrails, prompt injection, red-teaming, ethics, GDPR/India DPDP compliance

NeMo Guardrails. Adversarial robustness. India's DPDP Act vs EU AI Act. When should an agent refuse?

2 Lab Guardrails implementation + red-team attack-defend

Input/output filtering. 2-round attack-defense exercise with structured final report.

"An agent without guardrails is a liability, not an asset."
📝
Guarded agent + red-team report.
Module 12
Weeks 26-27
Production Deployment
2L Cost optimization, self-healing

Optimize agents like production software. Failure analysis, root cause diagnosis, auto-recovery.

2 Lab Failure Analysis + Robust agent

Testing + checks + optimization pipeline.

"A notebook demo is a prototype. An optimized robust agent is a product."
⚙️
Robust agent
Module 13
Weeks 28-29
Agent Economics & Applications
2L Industry apps (healthcare, finance, education), TCO/ROI, maturity models, enterprise integration

Where agents create real business value. Build vs buy. The future of agentic AI and your career.

2 Lab Domain agent design + business case workshop

Design an agent for a specific industry. Build TCO/ROI models. Peer critique.

"The best agent builders understand both the technology and the business."
📝
Business case + domain agent. Internal Exam 4.
Module 14
Weeks 30-32
Capstone Project
4L Scoping, architecture review, 2 implementation sprints, eval sprint, docs, presentation

Build a complete agent system end-to-end. Peer-reviewed proposals. Code reviews. Public demo day.

9 Lab Capstone development sprints (4 × 2-hour sessions)

Instructor consultations. Structured feedback within 24 hours. Final presentation with Q&A.

"Your capstone isn't a class project — it's your portfolio piece and your first real AI product."
🏆
Complete capstone + GitHub portfolio + verifiable certificate.
Prerequisites: A laptop with internet. That's it — we handle everything else.
TOOLS & FRAMEWORKS YOU'LL MASTER
Python
LangChain
OpenAI API
CrewAI
ChromaDB
LangGraph
DSPy
Pydantic
LangSmith
Python
LangChain
OpenAI API
CrewAI
ChromaDB
LangGraph
DSPy
Pydantic
LangSmith
AgentVidya

Frequently Asked Questions

No. We teach you all pre-requisites required to complete this course. We start from the fundamentals of LLMs and build up step by step.

The course is 8 months (32 weeks), online, fully self-paced. 14 modules, 63 lectures, 40 labs across 2 semesters.

Yes. You'll receive a verifiable digital certificate that employers can authenticate. It's not just a PDF — it has a unique verification id.

You'll build lab projects and capstone projects. Projects include RAG pipelines, multi-agent systems, MCP servers, production deployments, and red-team exercises. Post completion of course, you will get a live project opportunity. All go on your GitHub.

A Live project is a real world assignment where you solve a business problem with the skills you will learn in our course.

Python, OpenAI API, LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, ChromaDB, FAISS, LangSmith, and more.

While some content on YouTube has value, they are often piecemeals. It is very easy to run into Tutorial Hell and remain stuck on StackOverflow forever. We teach you to build. Every lesson ends with a working project. Plus you get a certificate, internship, and competition access.

The course platform works on mobile, but we strongly recommend a laptop/desktop for the coding projects. A basic laptop with 4GB RAM is sufficient.

You will receive clear step by step instructions to setup your laptop for successfully completing this course. We cover all major operating systems.

No, EMI option is not available.

Our course is designed for outcomes: verifiable certificate, GitHub projects, live internship experience, and competition participation. These are exactly what recruiters look for.

We offer a 7-day no-questions-asked refund policy. If the course isn't for you, email us within 7 days of purchase for a full refund.

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