The Bicycle That Taught Me My Agent Loop Had No Ground Truth
I built an autonomous vector graphics engine. Six AI reviews proved its loop had no ground truth. Here is the constraint-solving kernel that survived instead.
In June 2026, the author penned a design document for an Autonomous Semantic Vector Graphics Engine—an autonomous multimodal agent employing an LLM to decompose prompts into components, render them, and return semantic SVG output. Initially, reviewers deemed it a thoughtful brainstorm rather than a practical spec. Four months later, a working engine emerged, albeit not identical to the original design.
The document outlines four issues: context window bloat, mathematical and spatial hallucinations, the spaghetti vectorization problem, and the onion-skinning proposal. The author reorganized the project into a new key architectural pivot, reframing the system from LLM drawing vectors to an LLM planning semantically with a deterministic geometric execution engine.
However, the Zero-Coordinate kernel still couldn't produce recognizable topologies, such as a bicycle's two wheels joined by a frame, highlighting the limitations of the current architecture.
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