Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

GenAI & AI Agents for Java Spring Boot Devs | Copilot,Claude

Name: GenAI & AI Agents for Java Spring Boot Devs | Copilot,Claude
Rating: 4.5 (70 reviews)

Claude Code, GitHub Copilot, MCP, n8n, Google Antigravity, Prompt Engineering, AI Agents that Automate Real Dev Workflow

Bestseller

Highest Rated

Created byFaisal Memon (EmbarkX), EmbarkX Official

Last updated 6/2026

English

English [Auto],

What you'll learn

AGENTIC AI FUNDAMENTALS - Understand how LLMs, context windows, tokens and hallucinations work so you can build agents that are reliable in production
PROMPT ENGINEERING - Write production-grade system prompts, slash commands and memory files that keep your agents reliable across long agentic tasks
CLAUDE CODE - Master terminal-native agentic coding with filesystem ownership, slash commands, memory files, subagents and MCP server integration
GITHUB COPILOT - Use Agent Mode, Next Edit Suggestions, Copilot CLI, AGENTS md and Copilot Cloud Agent to automate real workflows inside GitHub
MCP SERVERS - Connect GitHub, and Filesystem MCP servers to give your agents real tools and secure them against prompt injection attacks
N8N & EVENT-DRIVEN ORCHESTRATION - Trigger agent workflows from GitHub and Slack using n8n and chain them with Claude Code for full automation
REAL AI AGENT BUILDS - Build a PR review agent, slow query finder and more each introducing a distinct agentic architecture
GENERATIVE AI TOOLS - Compare Claude Code, GitHub Copilot, Google Antigravity and Gemini CLI to pick the right tool for every engineering workflow

Course content

15 sections • 67 lectures • 10h 26m total length

Why this course exists - the gap between hype and real productivity6:24
Bridge the hype to real productivity for java spring boot devs by mastering fundamentals, learning ai accelerated development, and building ai agents that automate workflows and integrate tools without code.
AI Agents DEMO | AI Agents We Actually Build in the Course6:05
Course Repository0:02

Prompts used in this section0:02
What is Prompt Engineering? AI Agent Instruction Mastery6:03
Master prompt engineering by turning vague instructions into detailed prompts with clear context and goals, enabling AI models to deliver accurate, high-quality outputs for coding and agents.
Prompt Structure: Crafting Instructions Your AI Agent Follows9:11
Explore how to craft perfect prompts for AI agents by using role, persona, context, clear instructions, positive framing, and output format; tailored for Java Spring Boot developers.
Zero-Shot Prompting: AI Agent Instructions Without Examples5:31
Master zero-shot prompting, instructing an AI to perform tasks without examples. Compare it to one-shot prompting and the need for examples in classification tasks to improve accuracy.
Few-Shot Prompting: In-Context Examples for Smarter AI Agents4:10
Many-Shot Prompting: Large Example Sets for AI Agent Precision3:07
Explore many-shot prompting with large example sets to enhance AI agent precision in bug triage, using p0–p3 priorities, ample diverse seeds, and context window considerations.
Chain-of-Thought Prompting: Make AI Agents Reason Step by Step3:48
Explore chain-of-thought prompting that forces AI to think step by step, providing auditable, thought-through reasoning for session storage decisions between Redis and PostgreSQL.
LLM Hallucination: Why AI Agents Fail & How to Prevent It4:06

What is Claude Code? Agentic AI Coding in Your Terminal6:10
Reading Your Codebase with Claude Code AI Agent7:48
Setting Up Claude Code in IntelliJ IDEA with JetBrains AI13:06
Set up Claude code on your machine and in IntelliJ IDEA, install cloud code and Claude code plugin, configure path, authenticate with Anthropic, and use Claude code in JetBrains.
Your First Claude Code Session: Agentic AI Coding in Action8:17
Installing Claude Code in Visual Studio Code: Complete Setup Guide9:14
CLAUDE.md Explained: The Memory File That Powers AI Agents5:07
Background Tasks in Claude Code: Run Parallel AI Agent Jobs2:20
Launch and monitor parallel ai agent jobs in Claude Code by running background tasks in cloud code shells, viewing outputs, and switching between microservice shells.
Session Management with CLAUDE.md: Context Window Strategy9:23
Context Windows & Token Limits in Long AI Agent Sessions4:10
Debug Loop: Fixing Validation Issues with Claude Code Agent15:24
Fixing Real Production Bugs with Claude Code AI Agent6:54
AI-Powered Refactoring: Code Improvements with Claude Code8:04
Managing Claude Code Costs: Token Awareness for AI Agents3:28
Apply token awareness and cost monitoring for Claude in ai agents by using context and cost commands, compact history, and clear to control tokens, context window, and subscription stats.
Claude Code Skills: Build Reusable Commands for AI Agents12:16
Create reusable cloud code skills with a skill.md file to automate instructions. Invoke personal or project level skills in cloud code to explain code with diagrams, architecture, and knowledge transfer.
Agents & Sub-Agents with Parallel Work in Claude Code: Agentic AI in Depth8:23
Will AI Replace Developers? The Honest Truth for Engineers4:08
stay in control when using ai agents and avoid overprompting by breaking tasks into stepwise workflows. review ai-generated code to preserve fundamentals, architecture, and scalable performance in production.

What is Google Antigravity? Agentic AI IDE for Developers5:29
Explore Google's anti-gravity, an agentic development platform enabling autonomous agents to automate tasks. Learn about its desktop app, command line interface, integrated development environment, and software development kit.
Installing Google Antigravity IDE: AI Agent Setup Guide6:34
Project Setup in Antigravity: Configure Your AI Agent IDE1:33
AI-Powered Editor: Hands-On Agentic Coding in Antigravity9:12
Antigravity Skills: Reusable AI Agent Workflows in the IDE7:51

What is GitHub Copilot? Agentic AI Assistant Built Into Your IDE6:58
Explore GitHub Copilot, an agentic AI assistant embedded in your IDE, offering native GitHub integration, AI agents, autosuggest, and cross-platform support with token-based pricing and a free tier.
Installing and Setting Up GitHub Copilot in VS Code and IntelliJ6:03
Install and sign in to GitHub Copilot in your IDEs by adding the official Copilot plugin, authenticating with GitHub, and optionally installing the CLI for terminal use.
GitHub Copilot CLI: AI Agent Commands Straight from Your Terminal6:16
Inline Suggestions and Next Edit Suggestions: AI Coding in Real Time3:34
Discover how Copilot in your IDE delivers inline and next-edit suggestions for real-time Java API development, including adding a put mapping to update a post with id, title, and content.
Referencing Files and Context Window Management in GitHub Copilot6:43
Explore how GitHub Copilot chat uses the current file context, reference other files via hash, and manage the context window, usage, and modes (agent and plan) in UI and CLI.

What is MCP? Model Context Protocol for AI Agents Explained9:00
MCP + Context7: AI Agent That Fetches Live Dependency Versions12:36
Database AI Agent: Claude Code + MCP Server Querying MySQL34:11
Configuring MCP Servers in VS Code for Agentic AI Workflows9:55
Configuring MCP in Google Antigravity IDE for AI Agents7:37
Automated Git Commits: GitHub MCP Agent with Claude Code14:08
MCP Security: Least Privilege, Prompt Injection & AI Agents7:22
The Best MCP Servers That Actually Can Change How You Code8:07

Requirements

Basic understanding of any programming language — you don't need to be an expert
Familiarity with what Git is — you don't need to be advanced
A curious mindset and willingness to learn — no AI or ML background needed whatsoever

Description

Most AI courses teach you to use AI tools. This one teaches you to build AI agents that do engineering work autonomously — running in your CI pipelines, reacting to events, and fixing problems without waiting for you.

This is a hands-on course for freshers-mid-to-senior software engineers ready to move from AI user to AI agent builder.

You won't be building toy demos. Every agent in this course solves a real problem: automated PR reviews, slow query detection, with human approval gates, and more. Each build introduces a distinct architectural pattern you can adapt to your own stack.

What you'll learn

How LLMs actually work: context windows, hallucinations, token limits, and why it matters when you're building agents, not just chatting
Prompt engineering that holds up in production agent contexts
Claude Code: terminal-native agentic coding with filesystem ownership and MCP integration
GitHub Copilot: IDE-native and GitHub Actions agentic workflows
OpenAI Codex: async task delegation with PR-based output
Google Antigravity / Google Gemini: multimodal terminal input for Google ecosystem teams
MCP servers: extending your agents with custom tools and external integrations
n8n: event-driven orchestration that connects your agents to the rest of your workflow
JetBrains AI: Run all AI tools inside IntelliJ IDEA

Who this is for

Software engineers or Freshers who want to build and deploy real AI agents
Java and Spring Boot developers integrating AI into existing backend systems
DevOps and cloud engineers looking to automate repetitive operational work
Any developer tired of demos and ready to run agents in actual CI/CD pipelines

Requirements

Comfortable with at least one backend language (Java or Spring Boot experience is a plus)
No prior AI or machine learning experience needed

Who this course is for:

Any developer — fresher or experienced — who wants to understand AI agents and build real ones that solve actual engineering problems
Students and graduates looking to stand out by adding agentic AI skills to their portfolio before entering the job market
Working developers across any stack who want to start using Claude Code, GitHub Copilot and MCP servers beyond basic autocomplete

GenAI & AI Agents for Java Spring Boot Devs | Copilot,Claude

What you'll learn

Explore related topics

Course content

Welcome and Course Introduction3 lectures • 13min

Generative AI Foundations: LLMs, Tokens and Context Windows2 lectures • 30min

Prompt Engineering for Developers: Zero-Shot to Chain-of-Thought8 lectures • 36min

Claude Code: Agentic AI Coding in Your Terminal16 lectures • 2hr 4min

Google Antigravity IDE: Agentic AI in the Editor5 lectures • 31min

GitHub Copilot: AI Coding Agent in VS Code and IntelliJ IDEA5 lectures • 30min

Real Java development with AI in Loop | Building Spring Boot Features with AI3 lectures • 37min

MCP: Protocol that Makes Agents Possible | Connect AI Agents to Real Tools8 lectures • 1hr 43min

From AI Assistant to AI Agent: What Agentic AI Actually Means1 lecture • 13min

Pull Request Review AI AGENT: AI Agent That Automates Pull Request Reviews2 lectures • 35min

Requirements

Description

Who this course is for: