AI shopping assistants are increasingly steering consumer product discovery, creating a pressing need for seller‑side tools that measure competitive visibility and diagnose root causes. Our proposed system achieves this through three core steps:
- Define Agentic Share-of-Search (ASoS) as the decision metric, quantifying the share of a seller's products in AI‑mediated search results.
- Deploy query agents across major AI search platforms to automatically gather raw signals such as ranking positions and exposure counts.
- Introduce a ReAct‑based diagnostic agent that runs a think‑act loop on the collected signals and outputs a prioritized list of merchandising or marketing interventions.
A feasibility evaluation was conducted via 100 ablation trials. The diagnostic agent recovered the ablated signal in 39% of trials (95% CI: 30.0%‑48.8%, a 5.5× improvement over chance), rising to 63.9% for high‑correlation ablations.
Review: The system demonstrates the promise of multi‑agent collaboration in LLM‑mediated e‑commerce, yet further work is needed to ensure cross‑platform consistency and real‑time responsiveness.