Agentic Share-of-Search: A Multi-Agent AI System for Competitive Decision-Making in LLM-Mediated E-Commerce

A multi-agent AI system called Agentic Share-of-Search (ASoS) automates competitive visibility measurement and root-cause diagnosis in LLM-mediated e-commerce, according to an arXiv cs.AI paper by Spandan Ghose Chowdhury. The system deploys query agents across leading AI platforms and uses a ReAct-based diagnostic agent to recommend prioritized merchandising interventions. A 100-trial ablation study found the agent recovered 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.

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.