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
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
