Challenge GrassLobster – miro.imaginative and prescient
The geometric course of stays seen to you. The identical course of is mirrored as textual content for the agent.
Geometry Stations on the canvas
GrassLobster Session Components organise the mannequin into related Geometry Stations. Each Station runs the code for a significant job. You see the sequence and dependencies with out following each particular person operation.
A venture would possibly transfer by way of inputs, main geometry, structural logic, secondary geometry and output. The construction follows the duty. GrassLobster hides implementation complexity, not geometric logic.
Mirrored logic and inputs
Station definitions and executable code are mirrored in readable venture information. The agent can find the related logic, perceive related Stations and make focused adjustments by way of the supported workflow.
Supported enter values are mirrored too. Dimensions, counts or spacing will be modified with out rewriting the Station code; Grasshopper recalculates the associated geometry.
A folder with venture context
Agent Instructions information how the agent asks questions and works with you. Domain data provides ready topic steerage, your individual requirements or materials notes. Geometric references talk form by way of screenshots, research or instance fashions.
References can come from chat or designated venture folders. What the agent can use is dependent upon its instruments and the file format. This is venture context, not an robotically listed data system.
An agent that stays exterior
The venture is designed round interchangeable brokers, with the mandatory file and power capabilities. It shouldn’t be tied to at least one built-in mannequin. As appropriate brokers enhance at coding, planning and geometry reasoning, the identical venture construction can profit.
A path towards agentic optimization
Where a mannequin exposes measurable outcomes, an agent may differ inputs, evaluate outcomes and refine towards a aim — for instance, sustaining flooring space whereas lowering materials quantity.
This is a doable path, not a built-in general-purpose optimizer. It is dependent upon the mannequin, accessible outputs and agent instruments. Structural targets additionally require acceptable evaluation strategies.
“I wished to speak to my information.”
After round twelve years with Grasshopper, Miro Bannwart started working with AI brokers and wished to deliver that dialog into his parametric tasks.
Grasshopper already gave folks a visible strategy to perceive geometry. Agents wanted a illustration they might learn and alter. That led to textual content mirroring: conserving the geometric course of on the canvas whereas giving the agent entry to the identical venture by way of information.
