Build the engineering skills to put Claude and Claude Code to work on real systems. You'll choose the right Claude model for a job by weighing intelligence, speed, and cost, then design agent architectures that perceive, reason, and act. Using the Claude Agent SDK, you'll build production agents driven by stop-reason loops and engineer context strategies that keep long conversations affordable. You'll configure Claude Code for a multi-surface monorepo team, author reusable Claude Skills, and stand up a multi-shift quality monitoring system with layered orchestration. A capstone ties it together: you run, verify, and defend the design of four working systems. Experience with Python, Typescript, and APIs is assumed.
Overview
Syllabus
- Introduction to 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.
- Introduction to Claude 4 Model Architecture
- 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.
- Select Claude 4 Models with Claude API
- 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.
- Introduction to Agentic System Design
- Discover agentic AI: systems that perceive, reason, and act to achieve goals through a perception-reasoning-action loop, core components, and agent design patterns.
- Design Agent Architectures
- Learn to design agent architectures for automating complex tasks using multi-agent systems, enabling speed, specialization, resilience, and scalable AI-powered workflows.
- Introduction to Claude Agent SDK
- 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.
- Build Production Agents with Claude Agent SDK
- 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.
- Build a Claims Intake Agent with a stop_reason-Driven Loop
- 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.
- Engineer a Long-Conversation Context Strategy for a Retail Support Copilot
- 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.
- Introduction to Claude Code
- 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.
- Configure Claude Code
- Learn to build sophisticated AI systems by configuring Claude Code with modular agents and reusable skills for task delegation and domain-specific workflows.
- Introduction to Claude Skills
- Learn to create and structure Claude skills—modular files that encode expertise—enabling AI agents to perform tasks consistently to your team's standards.
- Create Claude Skills
- 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.
- Configure Claude Code for a Multi-Surface Monorepo Team
- 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.
- Build a Multi-Shift Quality Monitoring System with Claude Orchestration
- 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.
- Harness Engineering with Claude and Claude Code Project
- Stand up, run, and verify four reference systems from the course, then defend each one's design in a reflection brief grounded in your own run output.
Taught by
Abdellah Iraamane, Valerie Scarlata, and Sufian Kaki Aslam