Generative AI, LLMs, Prompt Engineering, AI Agents, Tool Calling, RAG, Embeddings, Vector Databases, MCP, LangChain, Python, n8n, API Integrations, Docker, Git/GitHub, Production Readiness & Deployment.
This program bridges the gap between traditional Generative AI and modern Agentic AI — teaching you how agents reason, use tools, remember context, retrieve knowledge, collaborate, and solve real business problems autonomously.
Generative vs. Agentic AI, the ReAct framework, agent types, and how autonomous systems are designed.
Design complete agent workflows visually with n8n — automation, routing, and AI nodes.
Vector embeddings, chunking, semantic search, and conversational retrieval over your own documents.
Go from zero Python to calling LLM APIs directly and building a ReAct agent from scratch.
Prompt templates, chains, tool-using agents, structured outputs, and code-based RAG pipelines.
Tool routing, structured output validation, and error handling within a single agent.
Why MCP exists, how LLMs use tools under it, MCP servers, and connecting AI clients to them.
Security, guardrails, error handling, self-healing pipelines, and privacy best practices.
Two full capstones — a Personal AI Representative and an Autonomous Research Agent — both shareable, demo-able, and resume-ready.
No coding background required. Non-technical participants build everything visually with n8n, while technical participants additionally go under the hood with Python.
Non-coders build everything visually with n8n. Technical participants additionally go under the hood with Python — same outcomes, two paths.
Every workflow built in class is packaged as an importable n8n template you keep, customize, and reuse — even commercially.
2 mini-projects + 1 capstone — all demo-able, shareable, and portfolio-ready by the end of the program.
n8n, OpenAI/Gemini APIs, RAG, LangChain, and the Model Context Protocol (MCP).
Support triage, invoice processing, knowledge bots, Telegram agents — the exact systems businesses pay to have built.
Beginner-friendly setup with guided notebooks and browser-based options wherever practical. Local setup is introduced gradually for Python projects, Docker, GitHub and deployment.
Experience the trainer's teaching style and course depth before enrolling — completely free, no commitment required.
A dual-track curriculum — start with beginner foundations, then move between no-code n8n automation and Python, completing every project block before switching tracks, and finish with two portfolio-ready autonomous AI systems.
| # | Session | What You'll Learn |
|---|---|---|
| 1 | Agentic AI Essentials | Understand agents, workflows, autonomy and the ReAct loop. |
| 2 | LLM Fundamentals | Learn tokens, context windows, hallucinations and model behaviour. |
| 3 | Prompt Engineering Foundations | Use zero-shot, few-shot, role and system prompting effectively. |
| 4 | Advanced Prompting + ReAct | Design reasoning prompts for tool use and multi-step tasks. |
| 5 | First Live API Interaction | Use API keys, JSON and a model response in a practical lab. |
| 6 | n8n Foundations | Explore nodes, triggers, credentials and visual automation. |
| 7 | Build Your First n8n AI Agent | Connect an LLM, define instructions and execute a live workflow. |
| 8 | Chatbot + Memory | Create a conversational agent with session memory and context. |
| 9 | Telegram AI Agent | Connect the agent to a real chat interface and trigger workflows. |
| 10 | APIs, JSON + HTTP Requests | Call services, authenticate requests and structure outputs. |
Beginner-first sequencing with live explanation, guided practice and recordings.
| # | Session | What You'll Build |
|---|---|---|
| 11 | Gmail Integration | Read messages, classify intent and create controlled email drafts. |
| 12 | Google Sheets Integration | Read and update structured data inside an automated workflow. |
| 13 | Capstone 1: Personal AI Representative | Build an n8n agent that uses memory, tools and your information. |
| 14 | Capstone 1: Integrate + Demo | Connect Telegram, Gmail or Sheets and demonstrate the workflow. |
| 15 | Python Setup + First Program | Install the environment and move confidently from visual to code. |
| 16 | Python Foundations I | Variables, data types, conditions, loops and core problem-solving. |
| 17 | Python Foundations II | Lists, dictionaries, functions and reusable program logic. |
| 18 | Files, APIs + Environment Variables | Work safely with packages, files, secrets and external services. |
| 19 | LangChain Foundations | Use prompt templates, chat models, chains and structured outputs. |
| 20 | LCEL + Structured Chains | Compose reliable chains and pass data between application steps. |
Capstone 1 delivers a working no-code or low-code AI representative.
| # | Session | What You'll Learn |
|---|---|---|
| 21 | Embeddings + Semantic Search | Turn text into vectors and retrieve information by meaning. |
| 22 | Vector Stores + RAG Foundations | Build grounded systems using chunking, retrieval and context. |
| 23 | RAG Implementation + Evaluation | Build a retrieval pipeline and test answer quality and relevance. |
| 24 | LangChain Agents + Tools | Create tools, bind them to an LLM and understand execution. |
| 25 | Tool Calling + ReAct Agents | Compare function calling with agent loops and controlled actions. |
| 26 | Advanced Agent Patterns | Tool routing, structured output validation, and robust error handling. |
| 27 | MCP Foundations + Integration | Connect AI clients to tools and private context through MCP. |
| 28 | Capstone 2: Autonomous Research Agent | Build a coded agent that searches, reasons, retrieves and reports. |
| 29 | Production, Security + Deployment | Add retries, guardrails, secret handling, testing and deployment. |
| 30 | Demo Day + Portfolio Launch | Present both capstones and leave with a roadmap for your next build. |
Final outcome: two demonstrable AI systems, a certificate and a portfolio launch plan.
Every participant completes both capstones — progressing from beginner-friendly automation to production-grade code.
Built with n8n, memory, Telegram and Gmail/Sheets. Connect tools, personal context and human review to integrate, then demonstrate a complete autonomous workflow.
Built with Python, LangChain, RAG, tools and MCP. Harden, test and deploy a portfolio-ready app that searches, reasons, retrieves and reports.
Everything you need to know before joining the program.
Engineer. Innovator. Builder of AI-powered Quality Engineering Workflows.
With over a decade of experience across QA, automation, and engineering strategy, Shashank has transformed quality from a mere "checkpoint" into a genuine competitive advantage. As an Engineering leader at a Fortune 500 financial services / IT company, he specialises in reimagining QA with AI, ML Ops, process automation, and intelligent test design — enabling teams to ship faster, smarter, and with confidence.
Having delivered 30+ training programmes and personally mentored 200+ students — including QA engineers, developers, and tech leads across the industry — Shashank brings a rare blend of corporate depth and teaching clarity. His learners have gone on to build and deploy real AI-powered systems inside their organisations. His hands-on, problem-first approach means every session connects directly to things you'll actually build at work.
Real feedback from professionals who completed the Agentic AI training and are now building real AI systems.
Every participant who successfully completes the training receives an official certificate from Isha Training Solutions — recognised by industry professionals.
Sample certificate — your name will be printed upon completion
To maintain the quality of our training and ensure a smooth learning experience for all participants, we do not allow batch repetition or switching between courses.
Moving from one course to another or shifting from one trainer to another (even if it is the same course) is not possible. Changing batches or trainers in any form is strictly not permitted.
We request all learners to attend the scheduled sessions regularly and make the most of their learning journey. Thank you for your understanding and continued support.
