AI-Agentic Coding with OpenClaw & ZeroClaw
Concept & Motivation
AI-coding started with Andrej Karpathy’s “vibe coding” — describe what you want, AI writes the code. In 2026, it has evolved into something more powerful and more structured: agentic coding, where multiple AI agents collaborate in orchestrated loops to plan, implement, test, and refine software.
Platforms like OpenClaw and ZeroClaw represent this evolution. They don’t just generate code — they run multi-agent development workflows where specialized agents (planner, coder, reviewer, tester) work together, catch each other’s mistakes, and iterate until the code meets quality standards.
This course teaches you to use these platforms effectively — and to understand when agentic coding accelerates development and when it creates technical debt.
What You’ll Learn
- The agentic coding paradigm — from code completion (Copilot) to code generation (ChatGPT) to agentic coding (multi-agent orchestration). What changed and why it matters
- OpenClaw deep dive — architecture, agent roles, orchestration patterns, and configuration. Hands-on with real development tasks
- ZeroClaw deep dive — zero-shot agent coordination, autonomous planning, and the differences from OpenClaw’s approach
- Multi-agent development loops — how planner-coder-reviewer-tester agent chains produce higher quality than single-agent generation. When to add agents and when more agents means more noise
- Prompt engineering for agentic coding — writing specifications that agent systems can decompose and implement. The difference between prompts that produce demos and prompts that produce production code
- Code review of agent output — what to look for when reviewing agent-generated code. Common patterns, common mistakes, and the specific quality issues agentic coding creates
- Integration with development workflows — fitting agentic coding into Git workflows, CI/CD pipelines, and team development practices. Branch strategies, review processes, and quality gates
- Comparison with Claude Code — when to use single-agent agentic coding (Claude Code) vs. multi-agent platforms (OpenClaw/ZeroClaw). The trade-offs between control and automation
- Cost and speed analysis — realistic metrics: how much faster is agentic coding? How much does it cost per feature? When does it break even vs. human development?
Who This Is For
- Software developers adopting agentic coding tools in their daily work
- Team leads and engineering managers integrating agentic coding into team workflows
- Technical founders accelerating product development with agent-powered coding
- Graduates of AI-Coding Mastery who want to level up to multi-agent development
Programming experience required. Language-agnostic, but examples are primarily in Python, TypeScript, and Rust.
Format & Duration
2-day workshop (on-site). Day 1: agentic coding concepts, platform deep dives, and guided development exercises with both OpenClaw and ZeroClaw. Day 2: participants tackle a real development task from their work using agentic coding, with peer review and comparison of approaches.
What Makes This Course Different
Most agentic coding content is either hype (“10x developer overnight!”) or narrow tutorials for a specific tool. This course gives you the conceptual framework to understand why multi-agent coding works, when it doesn’t, and how to integrate it into professional development workflows.
You’ll use both OpenClaw and ZeroClaw side-by-side, compare outputs, and develop your own evaluation criteria — so you can make informed tool choices as the landscape evolves.