Build AI Agents That Ship

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.