The geometric process stays visible to you. The same process is mirrored as text for the agent.

Geometry Stations on the canvas

GrassLobster Session Components organise the model into connected Geometry Stations. Each Station runs the code for a meaningful task. You see the sequence and dependencies without following every individual operation.

A project might move through inputs, primary geometry, structural logic, secondary geometry and output. The structure follows the task. GrassLobster hides implementation complexity, not geometric logic.

Mirrored logic and inputs

Station definitions and executable code are mirrored in readable project files. The agent can locate the relevant logic, understand connected Stations and make targeted changes through the supported workflow.

Supported input values are mirrored too. Dimensions, counts or spacing can be changed without rewriting the Station code; Grasshopper recalculates the related geometry.

A folder with project context

Agent Instructions guide how the agent asks questions and works with you. Domain knowledge adds prepared subject guidance, your own standards or material notes. Geometric references communicate shape through screenshots, studies or example models.

References can come from chat or designated project folders. What the agent can use depends on its tools and the file format. This is project context, not an automatically indexed knowledge system.

An agent that stays external

The project is designed around interchangeable agents, with the necessary file and tool capabilities. It is not tied to one built-in model. As compatible agents improve at coding, planning and geometry reasoning, the same project structure can benefit.

A path toward agentic optimization

Where a model exposes measurable results, an agent could vary inputs, compare outcomes and refine toward a goal — for example, maintaining floor area while reducing material volume.

This is a possible direction, not a built-in general-purpose optimizer. It depends on the model, available outputs and agent tools. Structural goals also require appropriate analysis methods.