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






