agentic brand
- role
- Originator, engineer
- date
- Apr 2026–present
- team
- Brand, Creative, Product
brand judgment before generation
Agents now make screens, emails, and copy. Without a brand’s stance, they fall back on the model’s defaults. I designed and built Ghost to give them that judgment before they start working.
Teams record their voice, principles, and decisions about trust alongside concrete materials in a .ghost/ package. Any compatible agent can read it. Designers can deepen that guidance without putting the whole brand into every task.
give the next agent the decision
Correcting one output doesn’t teach the next agent. I made the package a place to record the underlying judgment: when to celebrate, how to deliver bad news, or when brand presence should recede.
Authors write those decisions in Markdown, describe when they apply, and reference existing components, assets, or examples. The package travels with the repo.
a package and a selection protocol
I separated discovering the brand from loading its guidance. Ghost handles retrieval; the host agent makes the decisions.
Authors maintain
.ghost/ package
Brand stance · guidance · applicability · materials
User supplies
The task
A screen, email, sentence, or presentation to make.
- 01 / Ghost CLI
gather
Expose the menu
Every entry and when it applies. Full guidance stays in the package.
- 02 / Host agent
select
Match the task
The agent reads the menu and chooses the applicable entries.
- 03 / Ghost CLI
pull
Read selected guidance
Selected entries, material references, and the brand’s overall stance.
- 04 / Host agent
make
Apply the judgment
The agent interprets the guidance while creating the work.
Ghost makes no model calls. Unselected guidance stays available in the package, outside the active working context.
what agents make with it
Each brief below went through that selection flow against the same package. The outputs explore different situations and interactions within a shared design language.
less guidance to load
37%
Ghost’s selected guidance averaged 99.4 KB against a 156.9 KB DESIGN.md containing the full guidance. Across both brands and models, the reduction averaged 30.4%. Human designers reviewed all results and confirmed they met the quality bar.
Project site · Source on GitHub · Development log
Open-source development preview.