Multi-Agent Debate as a Strategic Decision-Support Tool: A Case Study of an AI-Powered Language-Learning Startup
Large Language Models (LLMs) are increasingly used by entrepreneurs for ideation, planning, and strategic analysis. In practice, however, many founders still use a single chat-based model, which can produce average, agreeable, and weakly documented recommendations. Multi-agent debate (MAD) offers a promising alternative by separating decision perspectives into independent role-based agents. This article examines whether a lightweight MAD workflow can operate as a practical decision-support and decision-governance tool for a resource-constrained startup. The article employs an exploratory single-case study design, drawing on a first-week decision log, founderprovided process artifacts, and structured qualitative coding of six decision sessions in an AI-powered languagelearning startup. The case shows that MAD can convert loosely framed strategic questions into documented decision records, surface cross-functional tensions, and accelerate founder synthesis. During the first operational week, six decisions were documented; the densest 48-hour period contained four decision sessions, while the full first-week log covered product activation, pricing, landing-page direction, and three feature-level PR/FAQ reviews. The study contributes to the emerging literature on LLM agents and AI-augmented strategic decision-making by shifting the empirical focus from benchmark tasks to a real entrepreneurial decision routine. The article also proposes a reusable five-part governance model: grounding pack, role files, independent agent execution, human synthesis, and decisionlog reuse.