Build the system around the work.
The same primitives I use across my own projects—context, tools, software, computers, agents, workflows, and verification—can be configured around a specific business, profession, dataset, or operating environment.
Tell me what you're working onA general-purpose agent is the starting point. The useful system is purpose-built.
I built this architecture around my own work first. ChatGPT became the interface; files, repositories, applications, computers, cloud infrastructure, and specialized agents became the execution environment.
Someone else should not get a copy of my setup. They should get the version designed around their work: their sources, permissions, workflows, tools, machines, approval gates, and verification rules.
Understand the problem
Start with the work as it actually exists: the objective, the information, the people, the software, the handoffs, and the point where it keeps breaking.
Build the first working version
Take one bounded workflow and make it real inside the systems you already use where possible. The output should be something that works, not a deck describing what might work.
Keep improving the system
For work that is continuous rather than project-shaped: maintain the context, automation, agents, software, and operating rules as the business changes.
Tell me what you are trying to do, what keeps breaking, and what a good result looks like.
I would rather understand the problem first than force it into a product or service category.