About This Course
<div>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.</div><div><br></div><div>This is a hands-on course for freshers-mid-to-senior software engineers ready to move from AI user to AI agent builder.</div><div><br></div><div>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.</div><div><br></div><div>What you'll learn</div><div><ul><li><span style="font-size: 1rem;">How LLMs actually work: context windows, hallucinations, token limits, and why it matters when you're building agents, not just chatting</span></li><li><span style="font-size: 1rem;">Prompt engineering that holds up in production agent contexts</span></li><li><span style="font-size: 1rem;">Claude Code: terminal-native agentic coding with filesystem ownership and MCP integration</span></li><li><span style="font-size: 1rem;">GitHub Copilot: IDE-native and GitHub Actions agentic workflows</span></li><li><span style="font-size: 1rem;">OpenAI Codex: async task delegation with PR-based output</span></li><li><span style="font-size: 1rem;">Google Antigravity / Google Gemini: multimodal terminal input for Google ecosystem teams</span></li><li><span style="font-size: 1rem;">MCP servers: extending your agents with custom tools and external integrations</span></li><li><span style="font-size: 1rem;">n8n: event-driven orchestration that connects your agents to the rest of your workflow</span></li><li><span style="font-size: 1rem;">JetBrains AI: Run all AI tools inside IntelliJ IDEA</span></li></ul></div><div><span style="font-size: 1rem;">Who this is for</span></div><div><ul><li><span style="font-size: 1rem;">Software engineers or Freshers who want to build and deploy real AI agents</span></li><li><span style="font-size: 1rem;">Java and Spring Boot developers integrating AI into existing backend systems</span></li><li><span style="font-size: 1rem;">DevOps and cloud engineers looking to automate repetitive operational work</span></li><li><span style="font-size: 1rem;">Any developer tired of demos and ready to run agents in actual CI/CD pipelines</span></li></ul></div><div><span style="font-size: 1rem;">Requirements</span></div><div><ul><li>Comfortable with at least one backend language (Java or Spring Boot experience is a plus)</li><li><span style="font-size: 1rem;">No prior AI or machine learning experience needed</span></li></ul></div>
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