Vibe Patenting: Agentic AI for Professional Patent Drafting

Professional patent drafting requires more than long-form legal text generation: an agent must extract inventive concepts from heterogeneous technical evidence, plan a protection strategy, coordinate claims, the specification, embodiments, and figures, and repeatedly check support and consistency. We present Vibe Patenting, an agentic AI framework for this end-to-end professional workflow. The system combines a structured Problem–Insight–Solution–Effect (PISE) invention representation with specialized patent skills, realized either as reusable modules orchestrated by a meta-agent or as an integrated domain-specialized patent agent. Across more than one hundred generated drafts from ten technical reports, stronger models and greater inference-time reasoning generally receive higher LLM-judge scores, and agentic configurations outperform general-purpose chat in our system-level comparisons. We also place a separate patent-QA LLM judge inside a multi-round revision loop: structured judge feedback continues to improve judge-assessed quality after generic revision begins to saturate. Independent evaluation by a professional patent attorney confirms a substantial aggregate quality gain after QA-guided revision while revealing strong metric dependence in judge agreement and calibration. A second, independently instructed QA evaluator further shows that revision gains can transfer beyond the optimizing judge even as evaluator-specific preferences emerge. These results position professional patent drafting as a testbed for agentic AI built from structured domain representations, reusable workflows, inference-time reasoning, and external evaluation.