• views: 247
• date: 7 June 2026
• posted by: 0nelove

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



   
   
   

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