Build AI Agents That Ship

Learn AI-assisted development the way you'll actually do it — inside one real codebase seeded with twelve deliberate bugs. Every lab runs three ways (Claude Code, Copilot, or a free tier), every fix has to be verified, and the final track has you measure your own before-and-after honestly.

Full agent lifecycle — design, build, protect, observe, deploy. 9 tracks from LLM fundamentals through multi-agent production systems. Includes certification prep for Claude Certified Architect.

Deep-dive into Claude Code — API essentials, prompt engineering, CLAUDE.md, permissions, subagents, hooks, tool use, MCP, and the Agent SDK. Fast path for engineers already comfortable with LLMs.

Build, secure, and orchestrate MCP servers from scratch. Covers resources, prompts, HTTP/SSE transport, multi-server orchestration, and production patterns for Claude Code and IDE integrations.

Stop prompting and start specifying — a field guide to specs Claude can build against. Three scenarios (feature, defect, migration) worked end-to-end on a healthcare prior-auth platform, with a runnable Spring Boot companion repo and four hands-on labs.

The same discipline, tool-assisted. OpenSpec keeps capability specs as living documentation and every change as a reviewable proposal that is archived back into them — across Claude Code, Cursor, Copilot and 30+ agents. Nine phases from setup through governance, in Python and Java.

Master the LLM context window end to end — design, assembly, retrieval, compression, optimization, and governance of everything you feed a model. The skill under every reliable agent.

From RAG limits to self-updating codebase brains — structural graphs (tree-sitter, Graphify), Google's Open Knowledge Format, MCP serving, and honest multi-arm token benchmarks measured on real repositories.

Master the discipline on an app that shows its tokens — MedFlow Copilot's Token Lens itemizes every AI call by layer, cost, and correctness. Ten strategies, thirty labs, and one law: savings only count when the answer is right.

Beginner-friendly track for Google's AI pair programmer in VS Code and JetBrains — completion, chat, GEMINI.md context, agent mode, MCP, Gemini CLI, GitHub PR reviews, Cloud Run deployment, and enterprise controls.

End-to-end track for Gemini CLI — prompting, GEMINI.md, skill files, plan mode, requirements, design, implementation, testing, deployment, MCP servers, extensions, GitHub Actions, and Google Cloud MCP.

Build production agents using open-source models. Covers the full agent stack — RAG, memory, multi-agent systems, guardrails, observability, deployment, and cost optimization — without vendor lock-in.

Build the same AI agent three ways — in the Anthropic (Claude), Google (Gemini), and OpenAI SDKs, side by side. Tool use, structured output, memory, RAG, multi-agent, and production, each mapped across all three so switching providers is a config change, not a rewrite.

Side-by-side analysis of Claude Code vs Gemini CLI vs GitHub Copilot. Covers setup, prompting, code generation, file ops, agentic workflows, MCP, CI/CD, cost, and how to pick the right stack.

The "Day 2" discipline for LLM systems already in production — evaluation, deployment, monitoring, incident response, drift, and governance. Keep agents reliable, observable, and accountable after launch.

Build the internal AI platform layer every team relies on — model gateway, prompt registry, guardrails, evaluation harness, cost controls, and compliance. Turn scattered AI experiments into shared infrastructure.