# Agentic Share-of-Search: A Multi-Agent AI System for Competitive Decision-Making in LLM-Mediated E-Commerce > Source: > Published: 2026-09-12 04:00:00+00:00 # Agentic Share-of-Search: A Multi-Agent AI System for Competitive Decision-Making in LLM-Mediated E-Commerce By Spandan Ghose ChowdhurySource: [arXiv cs.AI](https://arxiv.org/list/cs.AI/recent) arXiv:2609.11190v1 Announce Type: new Abstract: AI shopping assistants increasingly redirect consumer discovery, creating an urgent need for tools that support seller-side competitive decision-making. We present a multi-agent AI system that automates competitive visibility measurement and root cause diagnosis in [LLM](/glossary/llm)-mediated ecommerce. The system introduces Agentic Share-of-Search (ASoS) as the decision target, deploys query agents across leading AI platforms, and uses a ReAct-based diagnostic agent to recommend prioritized merchandising interventions. A 100-trial ablation study, presented as a feasibility[evaluation](/glossary/evaluation)of this prototype, shows the agent recovers the ablated signal in 39% of trials (95% CI: 30.0% - 48.8%, 5.5x over chance), rising to 63.9% among high-correlation ablations. Get AI news in your inbox Daily digest of what matters in AI.