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STILL DOING IT BY HAND? LET AI AGENTS DO THE WORK.

AI Agents & Agentic AI Mastery to Build Intelligent Autonomous Systems

Go from "what is an AI agent?" to designing, building, testing, securing and deploying real AI agents. Build them with code (Python, LangGraph, MCP) or without code (n8n), using free tools first.

  • 21+ hours
  • Beginner to Advanced
  • English
  • Certificate
  • 16 Modules
  • LangChain - LangGraph - MCP - CrewAI - n8n
  • Beginner to Advanced

What you'll learn

  • Understand what turns a chatbot into an agent, and decide when an agent is the right solution and when it isn't
  • Choose the right AI model for each task and estimate what an agent will cost to run
  • Write system prompts, examples and structured JSON output that make agents reliable
  • Give agents "hands" by building tools they can call safely to check slots, create bookings and draft quotes
  • Build a knowledge base with RAG, embeddings and vector stores, so agents answer from your own documents with citations
  • Apply proven agent patterns: ReAct, planning, reflection, routing and orchestrator-workers
  • Build stateful agents in LangGraph with memory and human approval before risky actions
  • Build an MCP server and connect it to Claude Desktop and your own agents
  • Compare Google ADK, the OpenAI Agents SDK and the Claude Agent SDK, and pick the right framework
  • Design multi-agent teams with supervisors and handoffs, and build one in CrewAI
  • Build a complete agent without code in n8n
  • Test agents with golden datasets and traces, protect them against the OWASP Agentic Top 10, and run them in production with monitoring and cost controls

Course content

16 sections · 102 lessons · 21h 30m total

Module 1: The Agentic Shift — What AI Agents Are7 lessons · 1h 15m
  • Chatbot, workflow or agent: knowing the difference10 min
  • The agent loop and the five building blocks of an agent10 min
  • How much autonomy to give an agent: a practical scale10 min
  • Measuring whether an agent is good10 min
  • When NOT to build an agent10 min
  • Lab: feel the difference between chat and tool-using AI15 min
  • Assignment: build an agent opportunity map10 min
Module 2: Inside the Agent's Brain — Models, Tokens & Costs7 lessons · 1h 15m
  • How language models turn text into decisions10 min
  • Today's model families and reasoning ("thinking") models10 min
  • Settings that control behaviour, such as temperature9 min
  • Lab: test models in Google AI Studio and get your free API key14 min
  • Estimating and cutting agent costs9 min
  • Model limitations every agent designer must plan for9 min
  • Optional lab: run a model on your own computer with Ollama14 min
Module 3: Instructing Agents — System Prompts & Structured Output7 lessons · 1h 20m
  • The messages an agent sees11 min
  • Anatomy of a strong agent system prompt11 min
  • Few-shot examples and step-by-step reasoning11 min
  • Roles, personas and boundaries11 min
  • Structured JSON output your code can trust10 min
  • Lab: build and test an intake classifier16 min
  • Common prompt failures, and treating prompts like code10 min
Module 4: Your Developer Workbench7 lessons · 1h 15m
  • Install Python and VS Code10 min
  • Virtual environments and packages10 min
  • Keep API keys secret with .env and .gitignore10 min
  • Lab: run your first coded model call15 min
  • Structured output with Pydantic10 min
  • Rate limits, errors and retries10 min
  • Using AI coding assistants responsibly10 min
Module 5: Tools & Function Calling — Giving Agents Hands6 lessons · 1h 25m
  • Why agents need tools, and how function calling works13 min
  • Defining tools: name, description, arguments13 min
  • Lab: build and test a booking agent20 min
  • The explicit tool loop that frameworks use13 min
  • Handling tool failures13 min
  • Tool safety rules that prevent disasters13 min
Module 6: Memory & Knowledge — RAG, Embeddings & Vector Stores7 lessons · 1h 25m
  • Short-term, working, long-term and knowledge memory12 min
  • Managing conversation context12 min
  • Embeddings and the RAG pipeline explained simply11 min
  • Chunking well, and choosing a vector store11 min
  • Build a knowledge base with Chroma11 min
  • Lab: build RAG with citations and test grounding17 min
  • Measuring retrieval quality, and long-term memory with privacy11 min
Module 7: Thinking Patterns — ReAct, Planning, Reflection & Routing6 lessons · 1h 15m
  • Start with the simplest design that works12 min
  • Workflow patterns: chaining, routing, parallel work12 min
  • ReAct, plan-and-execute and orchestrator-workers12 min
  • Reflection and evaluator-optimizer loops11 min
  • Lab: build a router and a reflection check before quotes go out17 min
  • Tree-of-thoughts and other advanced reasoning11 min
Module 8: Building with LangGraph6 lessons · 1h 35m
  • LangChain vs LangGraph: who does what14 min
  • Lab: a quick agent with create_agent20 min
  • Graph basics: state, nodes and edges14 min
  • Memory per customer, pausing and resuming14 min
  • Lab: build a graph with human approval20 min
  • Streaming progress, errors, limits and project structure13 min
Module 9: MCP — Connecting Agents to Any Tool or Data6 lessons · 1h 25m
  • The problem MCP solves: hosts, clients and servers13 min
  • Tools, resources and prompts; local vs remote servers13 min
  • Lab: build and test an MCP server with the MCP Inspector20 min
  • Connect your server to Claude Desktop and your LangGraph agent13 min
  • MCP security rules, registries and remote servers13 min
  • Agent-to-agent communication with A2A13 min
Module 10: Vendor Agent SDKs — OpenAI, Claude & Google ADK6 lessons · 1h 20m
  • One set of ideas across every SDK13 min
  • What to avoid in 2026: deprecated tools12 min
  • Lab: rebuild the agent in Google ADK on the free Gemini key19 min
  • OpenAI Agents SDK and Claude Agent SDK (optional, paid API)12 min
  • Microsoft Agent Framework overview12 min
  • A decision matrix for choosing a framework12 min
Module 11: Multi-Agent Systems — Teams, Handoffs & CrewAI6 lessons · 1h 25m
  • When you need more than one agent, and when you don't13 min
  • Supervisor, pipeline, handoff and critic architectures13 min
  • Designing roles and communication13 min
  • Lab: build and stress-test a CrewAI team20 min
  • How multi-agent systems fail13 min
  • Supervisors in LangGraph and handoffs in SDKs13 min
Module 12: No-Code Agents — n8n, Zapier & Make6 lessons · 1h 20m
  • Why and when to choose no-code13 min
  • Start n8n and learn the building blocks12 min
  • Lab: build an intake agent with memory and Google Sheets tools19 min
  • Add a human approval step12 min
  • Activate, monitor and stay safe12 min
  • Zapier and Make for agent workflows12 min
Module 13: Testing & Evaluating Agents6 lessons · 1h 15m
  • Why agent testing is different from normal software12 min
  • What to measure: outcomes, steps, content and cost12 min
  • Build a golden test dataset12 min
  • Code checks and LLM-as-judge11 min
  • Tracing every step in LangSmith11 min
  • Lab: build an evaluation suite with automated regression tests17 min
Module 14: Agent Security & Guardrails7 lessons · 1h 20m
  • Why agents are a new security problem11 min
  • Direct and indirect prompt injection11 min
  • The OWASP Top 10 for Agentic Applications11 min
  • Layered guardrails, permissions and sandboxes11 min
  • Human oversight that actually works10 min
  • Lab: guard and red-team your agent16 min
  • Responsible AI duties10 min
Module 15: Shipping to Production6 lessons · 1h 15m
  • Production readiness checklist12 min
  • Wrap the agent in an API and package it in a container12 min
  • Durable state, secrets and deployment options12 min
  • Monitoring, alerts, cost controls and fallbacks11 min
  • Model updates and incident response11 min
  • Lab: ship the agent as a service17 min
Module 16: Capstone — The Complete Agent Platform & Your Career6 lessons · 1h 25m
  • Assemble the complete platform13 min
  • Lab: end-to-end platform test20 min
  • Write a 30-day pilot plan13 min
  • Adapt the design to another industry or your own business13 min
  • Careers in agentic AI, and presenting your capstone as a portfolio project13 min
  • Final assessment13 min

Requirements

  • No AI experience needed
  • No coding needed for the non-technical path. Basic Python helps on the technical path, and Module 4 sets everything up from zero
  • A laptop or desktop (Windows, macOS or Linux) with a modern browser
  • A Google account for a free Gemini API key (no card needed)
  • A free ChatGPT or Claude account
  • Python and VS Code (free, installed step by step in Module 4)
  • n8n: free self-hosted version or a Cloud trial (Module 12)
  • You never need to spend money to finish this course. OpenAI and Anthropic API comparisons are optional and every paid lab has a free alternative

Who this course is for

  • Complete beginners who want a guided path into agentic AI, with a no-code option
  • Developers who want to build agents with Python, LangGraph, MCP and vendor SDKs, including testing and deployment
  • Business owners and managers who want to know where agents add value, what they cost and how to manage the risks
  • Automation and no-code builders who want to add AI agents to n8n, Zapier or Make workflows
  • Students and career changers who want a portfolio project and a clear path into agent-engineering roles

About this course

AI is evolving from tools that answer prompts into systems that plan, use tools, remember, check their own work and take actions under human supervision.

AI Agents & Agentic AI Mastery gives you a practical, structured path to understanding and building these systems. You don't just learn the ideas. You build one real agent platform, piece by piece, from a first tool call to a deployed, monitored and secured multi-agent system.

The whole course builds one project for one business. FixWell Home Services is a repair company in Bengaluru with 26 technicians. Its operations head, Kavya Menon, handles 140+ requests a day across WhatsApp, the website and phone. Customers wait hours for quotes, technicians get double-booked, and staff search 200-page manuals for answers. Module by module, you build the FixWell Agent Platform that fixes this.

What you'll build:

• An intake and FAQ agent that answers from verified policies and manuals (RAG)

• A booking agent that checks slots, creates jobs and drafts quotes using tools

• An MCP server that any MCP-compatible AI app can use

• A multi-agent dispatch team that triages, schedules and escalates

• A no-code intake flow in n8n

• An evaluation suite, guardrails and monitoring, so the platform is safe to trust

Every module follows the same rhythm: explain, why it matters, see it, do it, practise, check, fix common mistakes, and apply it to the project.

Tools you'll work with: LangChain • LangGraph • MCP • CrewAI • Google ADK • OpenAI Agents SDK • Claude Agent SDK • n8n • LangSmith • Chroma • Gemini • Ollama

Most agent courses stop at demos. This one is built around reliability from the first module: testing, security, human approval and cost control. You'll also learn when not to use an agent.

By the end, you'll have a working, documented agent platform, a security review, a deployment runbook and a portfolio capstone you can show employers or clients.

Enrol now and start building intelligent AI agents.

How it works

  1. 1

    Enroll in the course for ₹399

  2. 2

    Finish the lessons and projects at your pace

  3. 3

    Get a certificate you can verify by its ID

Frequently asked

Why take this course?

Every lesson is practical and ends in something you can use. You learn with the real tools, step by step, and track your progress on your dashboard.

What is the benefit for me?

You finish with working projects, a certificate you can share, and skills you can use at work or in your own business straight away.

How long do I have to finish?

Learn at your own pace. Your progress, grades and streak are saved on your account.

Do I get a certificate?

Yes. Complete every lesson and a certificate with a unique ID appears under Certificates, ready to download.

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