Applied GenAI & Agentic AI — Full Program

The complete path from Python to production agents

A comprehensive, hands-on program covering everything you need to design, build, ship and operate modern AI systems — Python, LLMs, RAG, agents, MCP, guardrails, LLMOps and no-code automation.

Intermediate → Advanced24 weeks · cohort basedLive cohort · projects · 1:1 mentorship · capstone65 modules

Enroll — ₹79,999 See curriculumRolling admissions — new batches every 6 weeks

What you'll learn

Ship production-grade LLM apps end-to-end
Design RAG and Agentic RAG pipelines with hybrid retrieval
Build multi-agent systems with LangGraph, CrewAI and AutoGen
Expose and consume tools via the Model Context Protocol
Add guardrails, evals, tracing and observability to LLM apps
Containerize, deploy and monitor LLM systems in production

Who it's for

Engineers building AI features into production products
ML/Data engineers moving into GenAI and agentic systems
Tech leads and architects designing AI platforms
Founders shipping AI-native products

Prerequisites

Working knowledge of Python (or willingness to ramp fast in Week 1)
Basic REST/API familiarity
Git and command line comfort

Tools & frameworks

PythonFastAPILangChainLlamaIndexLangGraphCrewAIAutoGenMCPLangSmithGuardrails AIDockern8nCursorClaude Code

Curriculum

11 tracks · 65 modules

Modern Python — AI assisted

Get fluent in production Python with AI pair-programming.

Module 1: Python Programming Essentials
Module 2: Advanced Data Structures, Asynchronous Patterns, and Logging
Module 3: Numerical Computing and Data Analysis with NumPy and Pandas
Module 4: Data Visualization with Matplotlib and Seaborn
Module 5: Building AI APIs and Backends with FastAPI
Module 6: Building AI Native Applications with Streamlit and Gradio
Module 7: AI Pair Programming Fundamentals with GitHub Copilot

Generative AI, Prompting and Content Engineering

Module 1: Generative AI and LLM Foundations
Module 2: Transformer Architectures and How LLMs Work
Module 3: Working with LLMs: APIs, SDKs, Parameters and Open-Source Models
Module 4: Prompt Engineering Essentials
Module 5: Advanced Prompting Techniques
Module 6: Context Engineering — Memory, Windowing and Retrieval
Module 7: Structured Outputs, Function Calling and Tool Use
Module 8: Prompt Optimization, Evaluation and DSPy

Building and Shipping LLM Applications

Module 1: Embeddings and Semantic Search
Module 2: Working with Vector Databases
Module 3: Developing RAG Systems
Module 4: Advanced RAG — Hybrid Retrieval, Re-ranking and Agentic RAG
Module 5: Building LLM Apps with LangChain and LlamaIndex
Module 6: Multimodal LLMs and Beyond
Module 7: Building and Deploying End-to-End GenAI Applications
Module 8: Securing LLM Applications — Guardrails, Safety and Prompt Injection Defense
Module 9: Evaluating GenAI Applications
Module 10: Fine-Tuning and PEFT

Autonomous Agentic AI

Module 1: Agentic AI Foundations and Agent Architectures
Module 2: LangChain Core — Chains, Memory and RAG
Module 3: LangChain Agents and Tool Use
Module 4: LangGraph — Stateful Workflows and Routing
Module 5: LangGraph — Cycles, Human-in-the-Loop and Persistence
Module 6: Multi-Agent Orchestration with CrewAI
Module 7: Multi-Agent Systems with Microsoft AutoGen
Module 8: Agentic RAG and GraphRAG for Agents
Module 9: Deep Agents — Reflection, Planning and Long-Term Memory

Advanced Agentic AI — MCP, Interoperability, Guardrails & Scaling

Module 1: Model Context Protocol — Architecture and Custom Servers
Module 2: MCP Ecosystem Integrations
Module 3: Agent Interoperability — A2A Protocol
Module 4: Agent Interoperability — ACP and ANP
Module 5: Evaluation and Tracing with LangSmith
Module 6: AI Guardrails and Safety — NeMo and Guardrails AI
Module 7: Fine-Tuning and Agent Performance Optimization
Module 8: Dockerizing and Deploying AI Agents

No-Code Automation Tools

Module 1: Agentic Workflows with n8n
Module 2: Workflow Automation with Zapier
Module 3: Building with Make
Module 4: No-Code Agentic AI with Flowise

LLMOps — Production AI Systems, Deployment and Monitoring

Module 1: Foundations of MLOps and LLMOps
Module 2: LLM Infrastructure, Tooling and the Open-Source Stack
Module 3: Deployment, Containerization and Scaling of LLM Systems
Module 4: Monitoring, Governance and Responsible AI in Production

Capstone Project

Design, build and deploy an end-to-end agentic AI application

Vibe Coding

Module 1: Vibe Coding Fundamentals and AI-Driven Development
Module 2: AI-Powered Development with Cursor AI
Module 3: AI-Native Software Development with Google Antigravity
Module 4: Accelerating Development with Amazon Q Developer

Elective — Natural Language Processing for the LLM Era

Module 1: NLP Foundations and Text Processing
Module 2: Feature Engineering and Text Representation
Module 3: Tokenization, Embeddings and Text Encoding
Module 4: Sentiment Analysis and Text Classification
Module 5: Neural Language Models and Sequence Modelling
Module 6: Transformers and the Path to Large Language Models

Elective — Claude Code with MCP: Agentic Development on the Anthropic Platform

Module 1: Introduction to Claude and the Anthropic Model Family
Module 2: The Anthropic API — Messages, System Prompts and Tool Use
Module 3: Claude Code and Agentic Development with Claude
Module 4: Claude with MCP

Frequently asked

How much time should I commit each week?+

Plan for 8–10 hours per week — 2 live sessions plus labs and project work.

Do I need prior AI experience?+

No. Working Python and basic API knowledge is enough — we go from foundations to advanced agentic systems.

What do I build in the capstone?+

An end-to-end agentic AI application of your choice — designed, built, containerized and deployed with monitoring.

Is placement support included?+

Yes — resume reviews, mock interviews and referrals into our hiring partner network.

Ready to join?

Rolling admissions — new batches every 6 weeks. Seats are limited.

Enroll in Applied GenAI & Agentic AI