aiqbench analysis
This research introduces Agentic Share-of-Search (ASoS), a multi-agent framework designed to automate competitive visibility measurement and root cause diagnosis specifically within LLM-mediated e-commerce environments. By deploying query agents across major AI platforms, the system demonstrates feasibility in recovering ablated search signals through ReAct-based diagnostic reasoning, offering a novel approach to seller-side decision-making as AI assistants reshape consumer discovery paths.
Agentic Share-of-Search: A Multi-Agent AI System for Competitive Decision-Making in LLM-Mediated E-Commerce
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-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 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.
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-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 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.
Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
arXiv:2609.11190 [cs.AI]
(or arXiv:2609.11190v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.11190 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 198 words
