Most AI content tells you what is possible. This week at Determination Development, we documented exactly how to build it — step by step, mistake by mistake, live result by live result.

What you are reading now is the capstone. A map of everything we covered, in the order it actually makes sense to build it. If you follow these seven steps, you will have a self-hosted executive AI agent running real workflows inside your business — not a chatbot, not a prompt toy, but an operator.

Every step below links directly to the full guide. Read this as the roadmap, then go deep on whichever step you are on.

Step 01

Install and Secure Your OpenClaw Foundation

Everything else in this stack sits on top of OpenClaw — a self-hosted AI assistant platform that gives your agent memory, skills, tools, and persistent workflows. Before you configure anything, you need a clean, security-tested install.

This guide covers the full setup process on an Intel Mac, including the security configurations most tutorials skip entirely. Get this right and the rest of the stack becomes straightforward.

Read the Full Setup Guide

Step 02

Run Claude for Free via GitHub Copilot

Before you start paying for API credits, there is a legitimate path to running Claude inside OpenClaw at no cost using your GitHub Copilot subscription. This is one of the most underutilized moves in the self-hosted AI space right now.

This guide walks through the exact model identifiers, config setup, and what to expect from the github-copilot/claude-opus-4.5 integration — including the thinking model variant.

Read the Free Claude Guide

Step 03

Equip Your Agent With Skills

A raw OpenClaw install is capable but unspecialized. Skills are what turn a general-purpose agent into a domain expert — giving it the tools, context, and workflows for specific jobs like research, content, analytics, or client work.

This guide compares the ClawHub marketplace approach against building your own custom skills, and when each makes sense depending on your use case.

Read the Skills Guide

Step 04

Automate Daily Intelligence With Cron Jobs

An executive that only works when you ask is still just a tool. Cron jobs are what give your agent a schedule — letting it run research, compile reports, and deliver daily briefings without you lifting a finger.

This guide shows how to build a daily AI alpha report bot using OpenClaw cron jobs, including the exact configuration and output format that makes it actually useful.

Read the Cron Jobs Guide

Step 05

Turn Your Agent Into an Analytics Reporting Machine

Data without interpretation is just noise. This step covers how to configure your OpenClaw agent to pull, analyze, and report on performance data — turning raw numbers into strategic decisions your business can actually act on.

A real CTO does not just produce content. It tracks what is working, surfaces what is not, and keeps the operation accountable to the numbers.

Read the Analytics Guide

Step 06

Connect a Publishing Rail With Publer

Research and drafting mean nothing if content stays in a queue. This step connects your OpenClaw agent to Publer — giving it a live publishing rail so it can move content from idea to scheduled post to live distribution without manual handoff.

We documented the real gotchas: the workspace ID vs. user ID confusion, the User-Agent header requirement, and how to handle X's duplicate content policy. All of it is in the guide.

Read the Publishing Guide

Step 07

Install the 8 Core Commitments Framework

The technology is only half the equation. The other half is the operating agreement between you and the agent — the commitments that define what it works on, how it learns, what it reports, and what you still own as the human in the relationship.

The 8 Core Commitments framework gives your executive AI agent a mission, a rhythm, and a standard. Without it, even a perfectly configured stack drifts back toward occasional novelty.

Read the Commitments Framework

What You Have When All Seven Steps Are Done

You have a self-hosted AI agent with persistent memory, a custom skill set, a daily intelligence schedule, live analytics reporting, and a connected publishing rail — all running on a model that costs you nothing extra if you have GitHub Copilot.

More importantly, you have a system that runs whether you are at your desk or not. That is the difference between using AI and operating with AI.

This is not theoretical. Everything in this roadmap was built, tested, and documented live this week. The mistakes are real. The wins are real. And the stack is real.

Where This Stack Can Take You

Once your executive AI agent is operational, the question becomes what mission you point it at. We documented one strategic roadmap: using the full stack to generate enough revenue to acquire two 100 oz silver bars a month — consistently, on a system that runs itself.

Read: The Silver Formula →