OpenClaw Engineer | Guide DAO

Starts April 15

Track

OpenClaw Engineer

Stop using AI as a chat tab and learn to run a persistent agent that keeps context, uses tools, spins up subtasks, and comes back with results.

Access to a community of operators and builders working on agent systems

A lot of hands-on work and live configuration on real OpenClaw setups

Mentors, checkpoints, and architecture reviews

Lifetime access and updates for a stack that keeps changing

TrackOpenClaw is a course about moving from AI chat to an AI operating system. While everyone argues about which model is smarter, the real edge has already shifted to the orchestration layer: who can write instructions for agents, keep memory clean, connect tools, constrain permissions, choose models, and review output. We study OpenClaw not as another AI tool, but as an agent operating system for real work.

Deploy OpenClaw locally and on a VPS, then connect channels and model providers
Build a context pack with SOUL.md, AGENTS.md, USER.md, and TOOLS.md
Install ready-made skills and write your own SKILL.md
Set up delegate agents, handoff flows, and review loops
Connect real integrations and long-running workflows for work and life
Understand memory, context budget, routing, latency, and cost control
Ship a capstone system that does work instead of just generating text

OpenClaw Engineer

For people who want more than prompts: they want to build and run agent systems.

New

Developers who want to move from chat assistants to persistent agent systems

Pro

Founders and solo builders who want one natural interface over many tools and routines

Product, ops, and research people who want to automate long-running workflows

Power users who want to assemble a personal AI OS

Anyone who wants to understand the architecture, limits, and economics of agent systems

Program

We start by setting up OpenClaw and context discipline, then move into skills and orchestration, and finish with security, cost, and capstone scenarios.

Why OpenClaw, why now

Setup and core infrastructure

Memory, context, and personality

Skills and ClawHub

Delegate agents and multi-agent orchestration

Platform

The course is built so you do not just watch content, but assemble a working agent system piece by piece. Every module ends with an artifact: a skill, a context pack, a policy file, an integration, a delegate, or a capstone scenario.

Microformats
Short blocks that still go deep enough to fit a packed schedule.
Active learning
Less passive watching, more building, debugging, config comparison, and live review.
Code first / system first
You learn not only to write code, but to design memory, roles, constraints, and working loops for agents.

CommunityIn the agent layer, the bottleneck is increasingly not the model but the operator. That is why the key part of the course is not the recordings alone, but an environment where you can see how people actually build agents, break them, fix them, and push them toward production-like behavior.

Operator community

Live builds and OpenClaw breakdowns

We build agents live, from the first configuration to advanced multi-agent scenarios.

AMAs and architecture reviews

Market and infrastructure reviews

Career outcomes

The goal is not for you to leave with an AI certificate, but with a set of cases that proves you can design and operate agent systems.

Mock interviews / architecture reviews

We prepare you not for theory questions, but for conversations about real decisions: memory, orchestration, security, cost, and observability.

A portfolio people can actually see

A repo, skills, policies, workflow demos, and a final capstone sell better than an abstract resume.

Cases and demo-ready projects

By the end of the course, you should have a system you are comfortable showing to a team, client, employer, or partner.