(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.)
The course makes AI feel learnable first and powerful next. You understand how LLMs read tokens, why context windows matter, why hallucinations happen and how prompts become structured messages. Step by step, you move from simple concepts to visual agents in n8n, then into Python, FastAPI, advanced LangChain, RAG, MCP, Docker and deployment.
This AI Agents Engineering Course is designed to feel approachable on day one and powerful by the end. You learn the foundations, the automation layer, the Python layer, the backend layer, the retrieval layer, the protocol layer and the deployment layer.
Understand how agents perceive inputs, choose actions, use tools, remember context, retrieve evidence and stop safely when a goal is complete.
Work with Python projects, FastAPI backend APIs, Streamlit interfaces, JSON contracts, environment variables, package files, GitHub and cloud deployment habits.
Learn grounding, structured outputs, validation, retries, secret hygiene, human approval, LangSmith tracing, Docker packaging and practical boundaries for safe AI systems.
Every session gives you a concrete mental model, a working demonstration and a practical skill you can carry into the next build. You are not memorizing tool names; you are learning how agentic AI systems are designed, connected, tested, traced and shipped.
The course explains the "why" behind tokens, context, hallucinations, prompts, tools, memory and retrieval before asking you to build with them.
Concepts become real through workflows, APIs, Python projects, UI screens, backend endpoints, vector search, MCP tools and capstone application flows.
You learn when to use a workflow, when to use an agent, when to retrieve knowledge, when to ask for approval and how to trace what the AI actually did.
By the end, you can explain, build, debug and deploy practical AI agents from scratch, with a clear understanding of where each part fits and why it matters.
The program connects the tools used in modern AI agent development with the engineering concepts behind them, so you learn both what to use and how the system works under the hood.
OpenAI API calls, role-based messages, system prompts, zero-shot prompting, few-shot prompting, ReAct prompting, structured JSON responses and streaming output.
n8n workflows, triggers, credentials, AI Agent node, Google Sheets, Google Drive, Gmail draft workflows, Telegram, webhooks and HTTP Request patterns.
Python fundamentals, FastAPI endpoints, request/response schemas, service functions, environment variables, uv, requirements, .gitignore and backend project structure.
LangChain prompt templates, chat models, LCEL chains, parsers, custom tools, function calling, routing, agent orchestration, multi-agent workflows and LangSmith traces.
RAG concepts, chunking, embeddings, semantic search, Supabase vector storage, pgvector matching, metadata, citations, one-document RAG and multi-page PDF RAG.
MCP tools, resources, prompts, Inspector testing, FastMCP servers, Docker containers, Git, GitHub, Streamlit/FastAPI deployment paths, cloud secrets and production checks.
Built for learners who want practical AI agent engineering ability and want their learning time to create real technical confidence, even if they are starting with no AI background.
Developers who want to build AI agents, integrate LLM APIs, create backend services with FastAPI, and work with Python-based AI applications.
QA and automation professionals who want to use AI for test automation, support triage, structured outputs, tool calling, and intelligent workflows.
DevOps, cloud, and platform engineers interested in AI automation, Docker-based deployment, APIs, observability, and production AI systems.
AI, ML, and data professionals who want hands-on experience with RAG, embeddings, LangChain, agent orchestration, and AI application development.
Architects and technical leaders who want to understand AI agent architecture, MCP, backend integration, retrieval systems, and production design.
Professionals interested in workflow automation who want to build practical AI solutions using n8n, APIs, agents, tools, and integrations.
Beginners with basic technical familiarity who want a structured, hands-on path from visual AI workflows to Python, RAG, agents, and deployment.
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.
Nine portfolio-ready mini-builds plus a full capstone application — all demo-able, shareable, and resume-ready by the end of the program.
n8n, OpenAI/Gemini APIs, RAG, LangChain, FastAPI, Docker, and the Model Context Protocol (MCP).
Support triage, invoice processing, knowledge bots, Telegram agents — the exact systems businesses pay to have built.
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.
| Days | India Timings (IST) | US Timings (EST) | UK Timings (BST) |
|---|---|---|---|
| Demo | 16th September @ 9 PM - 10 PM (IST) | 16th September @ 11:30 AM - 12:30 PM (EST) | 16th September @ 4:30 PM - 5:30 PM (BST) |
| Day 1 | 18th September @ 9 PM - 10 PM (IST) | 18th September @ 11:30 AM - 12:30 PM (EST) | 18th September @ 4:30 PM - 5:30 PM (BST) |
| Day 2 | 21st September @ 9 PM - 10 PM (IST) | 21st September @ 11:30 AM - 12:30 PM (EST) | 21st September @ 4:30 PM - 5:30 PM (BST) |
| Day 3 | 23rd September @ 9 PM - 10 PM (IST) | 23rd September @ 11:30 AM - 12:30 PM (EST) | 23rd September @ 4:30 PM - 5:30 PM (BST) |
| Every Monday, Wednesday & Friday — 1-hour sessions will be conducted. | Every Monday, Wednesday & Friday — 1-hour sessions will be conducted. | Every Monday, Wednesday & Friday — 1-hour sessions will be conducted. |
A curriculum built for serious AI agent capability — grouped into connected engineering blocks that match the classroom decks and hands-on builds, so each topic adds a usable layer to your final agentic application skillset.
| Session | Topic | What You'll Learn |
|---|---|---|
| Demo | Master Agentic AI: Build Autonomous Systems | n8n basics, business workflow automation, support/email triage, visual logic, Python transition, RAG preview, portfolio assets, recordings and certificate overview. |
| S1 | What is Agentic AI? | Generative vs Agentic AI, workflow vs agent, brain/tools/memory/goal loop, ReAct cycle, agent types and a Jira-to-test-case n8n demo. |
| S2 | How LLMs Actually Work | Tokens, context windows, hallucinations, temperature intuition, prompt roles and live notebook demonstrations that make model behavior visible. |
| S3 | Prompt Engineering | Zero-shot vs few-shot, chain-of-thought style reasoning, ReAct prompting, context engineering, system prompts and tool calling with live business examples. |
| Session | Topic | What You'll Learn |
|---|---|---|
| S4 | API Lab | API keys, OpenAI setup, JSON fundamentals, hello-world model call, response traversal, structured output, native tool calling and streaming. |
| Session | Topic | What You'll Learn |
|---|---|---|
| S5 | Telegram AI Agent in n8n | BotFather setup, Telegram trigger, AI Agent node, chat ID mapping, reply node, simple memory, memory retention choices and troubleshooting. |
| Session | Topic | What You'll Learn |
|---|---|---|
| S6-7 | Python Basics Bridge | Variables, strings, input, lists, dictionaries, loops, functions, return values, typed style, OpenAI hello-world function and fast troubleshooting. |
| S8-9 | Python Project Bridge | Project folders, helper modules, pathlib, reading files, writing/appending text, JSON files, .env, uv, requirements, .gitignore and simple tests. |
| Session | Topic | What You'll Learn |
|---|---|---|
| S10 | LangChain Hello World Chain | Safe API setup, prompt-model-chain mental model, ChatPromptTemplate, LCEL, StrOutputParser, summary chain, debugging and local Ollama workflow. |
| S11 | LangChain Agents, Tools and Function Calling | Chain vs agent, Python tools, structured tool calls, model-selected actions, sequential tool execution, utility agent and LangSmith trace inspection. |
| Session | Topic | What You'll Learn |
|---|---|---|
| S12 | Streamlit Web Search Agent | Streamlit UI basics, Tavily search tool, search-vs-stable-answer decisions, agent integration, source-backed responses and trace review. |
| Session | Topic | What You'll Learn |
|---|---|---|
| S13 | Agentic RAG in n8n and Supabase | RAG need, embeddings, semantic search, chunking, metadata, Supabase vector store, ingestion workflow and RAG question-answering agent. |
| S14 | Voice Front End, APIs, Webhooks and Local n8n | Reusing the RAG brain through webhooks, CrudCrud POST/GET API practice, voice front-end integration and Docker-based local n8n concepts. |
| S16 | One-Document RAG | Visible split-embed-store-retrieve-generate loop, chunk size, overlap, Supabase rows, top-k retrieval, metadata and grounded answers. |
| S17 | Multi-Page PDF RAG | PDF inventory, text/table/image page handling, duplicate cleanup, searchable records, chunking, citations and Streamlit upload-train-ask flow. |
| Session | Topic | What You'll Learn |
|---|---|---|
| S15 | Docker, Local n8n and Pydantic Structured Outputs | Persistent local n8n setup, verification workflow, raw dictionary problems, Literal fields, Pydantic validation and structured model extraction. |
| Session | Topic | What You'll Learn |
|---|---|---|
| S18 | Git, GitHub and Streamlit Cloud Deployment | Git states, commits, GitHub repository, requirements.txt, .gitignore, Streamlit secrets, local deploy checks and public app deployment. |
| S19 | MCP Part 1: First Python Server | Why MCP exists, host/protocol/server/tools mental model, first calculator server, Inspector testing, multiple tools and Supabase document tools. |
| S20 | MCP Part 2: Tools, Resources and Prompts | Action/data/instruction classification, tool server, resource server, prompt server, full employee server, beginner authorization and inspection workflow. |
| S21 | AI IT Helpdesk Capstone Part 1 | Business boundary, Pydantic triage, Supabase RAG, local system-status tool, LangChain agent, prompt rules and brain-only Streamlit app. |
| S22 | AI IT Helpdesk Capstone Part 2 | SQLite ticket store, FastMCP support server, MCP-to-LangChain adapter, human approval flow, LangSmith tracing, Streamlit UI, FastAPI backend extension, Docker packaging and deployment runbook. |
The same course supports different learner goals because the curriculum is layered from fundamentals to deployable projects and advanced agent architecture. You can see your growth from first concept to full application.
Start visually with n8n, Telegram, Google integrations, webhooks, workflow branching, memory, RAG workflows and local n8n practice with Docker.
Move into Python project structure, API usage, LangChain chains, tools, agents, Streamlit interfaces, RAG pipelines and MCP servers.
Go deeper into tool boundaries, grounding, validation, observability, deployment secrets, approval flows and architecture patterns for internal AI applications.
The projects are chosen to show progressively stronger engineering patterns: trigger, memory, tool use, retrieval, validated output, protocol integration, backend APIs and deployment.
Manual trigger, Jira issue retrieval, model-generated JSON, information extraction and Google Sheets row creation.
Telegram trigger, AI Agent node, chat ID mapping, reply node, simple memory and retention choices.
Intent classification, routing logic, sentiment/urgency extraction, record updates and controlled reply drafts.
User input, validation, Tavily tool, agent decision, concise source-backed answer and trace inspection.
Google Sheets ingestion, chunk creation, embeddings, Supabase vector store and RAG answer workflow.
Upload, chunk, embed, retrieve top passages, answer from context and inspect sources through Streamlit.
Python tools, readable resources, reusable prompts, Inspector testing and integration into agent workflows.
Connect a user-facing interface to backend API endpoints, LangChain orchestration, RAG retrieval, tool calls, trace logging and deployment-ready configuration.
Structured triage, Supabase RAG, local tools, MCP ticket actions, SQLite, human approval, tracing and Streamlit UI.
The course is designed for learners who may not know AI yet, while still giving technical participants serious implementation depth across agents, RAG, MCP, FastAPI, Docker, LangSmith and deployment.
No AI background or advanced Python background is required. Basic computer comfort, willingness to follow live demos and curiosity about automation, APIs and AI systems are enough to begin.
Concepts are introduced visually first, then demonstrated in n8n, notebooks, Python projects, Streamlit apps and deployment workflows so every idea has a build attached to it.
You leave with working examples, architecture vocabulary, debugging habits and portfolio-ready builds that show you can build AI agents from scratch.
You see how a serious AI agent system is assembled beyond a notebook: a usable interface, backend API layer, agent orchestration layer, retrieval layer, MCP tool layer, observability layer and deployment path.
Build app screens that collect user input, show validation, display agent responses, expose approval buttons, support ticket lookup and make the AI workflow usable for non-technical users.
Design backend endpoints for agent requests, document search, ticket actions and status lookup using clean request schemas, response models and service boundaries.
Move from chains to advanced agents that can call tools, route tasks, coordinate retrieval, handle multi-step execution and support multi-agent workflow patterns.
Expose actions, data and reusable prompts through MCP servers so agents can use business capabilities through a standard, inspectable interface.
Trace prompts, model calls, tool calls, retrieved context, errors, latency and final responses so agent behavior can be inspected and improved.
Package local services, understand containers and volumes, prepare environment variables and follow end-to-end deployment paths for UI and backend apps.
The final build combines the major course skills into one realistic internal-support application.
The helpdesk agent answers approved knowledge questions, checks service status, recommends escalation when needed, creates tickets only after explicit approval, stores demo tickets in SQLite and supports ticket status lookup. The production extension shows how the same logic can sit behind a FastAPI backend, be traced in LangSmith and be prepared for Dockerized deployment.
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 training and are now building real AI systems.
The n8n module blew my mind — I built a fully automated lead nurturing workflow in day 3 without writing a single line of code. Then moving to Python and LangChain felt like a natural progression. Best AI course I've taken.
I joined as a manual QA tester with zero AI background. The RAG module and vector DB section opened my eyes to what's possible. The capstone project I built is now part of my portfolio and has helped me land a new role.
The MCP section was something I hadn't found in any other course. The trainer clearly knows the industry — every module had real-world examples directly applicable to enterprise projects.
Joined from the US batch — the timing worked perfectly. The Telegram AI assistant project was the best part. I deployed a fully working AI assistant for my team within a week of completing the course.
I've done 4 AI courses before this. None of them covered the Model Context Protocol with real code. This program actually builds your confidence — not just theory. The live Q&A sessions were incredibly valuable.
From n8n automation to a full FastAPI and LangChain backend — this program is genuinely end-to-end. The capstone I built is something I'm proud to show in interviews. This training changed the direction of my career.
Practical answers for learners evaluating the AI Agents Engineering Course.
