Harness Engineering with Claude and Claude Code

Set up your workspace for the course: configure your Anthropic API keys, get Claude Code running, and preview the harness engineering skills you'll build.

Explore the Claude 4.5 model family—Haiku, Sonnet, and Opus—each optimized for a different balance of intelligence, speed, and cost for efficient AI applications.

Learn to select the best Claude 4 model—Haiku, Sonnet, or Opus—using the API, matching model strengths to task complexity for faster, cost-effective, and high-quality AI solutions.

Discover agentic AI: systems that perceive, reason, and act to achieve goals through a perception-reasoning-action loop, core components, and agent design patterns.

Learn to design agent architectures for automating complex tasks using multi-agent systems, enabling speed, specialization, resilience, and scalable AI-powered workflows.

Learn to build autonomous AI agents with the Claude Agent SDK—set goals, assign tools, manage permissions, and automate complex workflows using natural language instructions.

Learn to build production AI agents with Claude Agent SDK that use tools to read, process, and write files, automating complex document workflows with clear prompts and structured outputs.

In this lesson, you'll build a working claims intake agent across three cumulative exercises, starting from a stop_reason-driven agentic loop and ending with a model that decides on its own when to clarify, route, or escalate.

In this lesson, you'll build a context-engineering harness for a long-running retail support copilot — pruning verbose tool output, keeping a persistent block of case facts, compressing resolved conversations on a token budget, and assembling it all so the facts that matter sit where the model reads them best.

Discover how to use Claude Code as an agentic AI assistant—reading your project, writing code, scaling tasks with subagents, and applying custom knowledge via CLAUDE.md and skills.

Learn to build sophisticated AI systems by configuring Claude Code with modular agents and reusable skills for task delegation and domain-specific workflows.

Learn to create and structure Claude skills—modular files that encode expertise—enabling AI agents to perform tasks consistently to your team's standards.

Learn to create and use Claude Skills to give agents reusable expertise. Build agents that discover, combine, and apply skills for complex tasks, with structured, type-safe outputs.

In this lesson, you'll stand up a version-controlled Claude Code configuration for a 12-person retail team working in one monorepo — a modular CLAUDE.md hierarchy with @import standards, path-scoped rules that load per file surface, a read-only /review slash command, a forked /deploy-check skill that keeps its verbose output out of the main session, and a decision doc that names when to use plan mode, direct execution, and the Explore sub-agent. You assemble it across four cumulative exercises, and a Python validator certifies the finished configuration against 35 acceptance tests.

Across four cumulative exercises, you'll assemble a Layer 3 Claude orchestration that wakes up on a schedule, looks at only what changed since last time, and survives a mid-run crash without losing its work: tiered hot/warm/cold state with atomic writes, a push-work-down invocation pipeline, an fsync'd manifest with a resume-vs-fresh recovery rule, and a fork-with-merge scratchpad workflow.