mlsanityData to 7 Oct 2026

Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement

arXiv 2608.18543 ↗First posted 19 Aug 2026cs.AIcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive customer re-engagement. This is particularly challenging for exploratory intents such as best smartphones or latest 5G phones, where users may leave the platform for external research before purchasing. We present a scalable, production-deployed framework that bridges search and CRM workflows through AI-powered Product Research Agents. The system identifies users with exploratory purchase intent and low engagement, conducts grounded multi-agent product research using behavioral signals, external knowledge, and enterprise catalog data, and delivers personalized recommendations through WhatsApp. We evaluate the framework in a 23-day production deployment involving approximately 15K WhatsApp notifications for mobile product discovery. The campaign achieved substantial CTR improvements over traditional WhatsApp recommendation campaigns, with evidence of secondary engagement through message forwarding and sharing. The deployment also generated downstream purchases and GMV impact, demonstrating the practical effectiveness of AI Product Research Agents for proactive customer re-engagement and end-to-end customer journey optimization.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 7 of its 10 references that are in the map. First posted on arXiv 19 Aug 2026.

Builds on

7 of 10 references.

Closest ancestors · 1

Same direction, newest first

  1. 2026ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation27 Feb

Other references · 6

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agentscited by 329
  3. 2024MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolutioncited by 132
  4. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendationscited by 130
  5. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 49
  6. 2026Benchmarking Multi-Agent LLM Architectures for Financial Document Processing: A Comparative Study of Orchestration Patterns, Cost-Accuracy Tradeoffs and Production Scaling Strategiescited by 6

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  3. 2026Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Study
  4. 2026Agentic Share-of-Search: A Multi-Agent AI System for Competitive Decision-Making in LLM-Mediated E-Commerce
  5. 2026ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping
  6. 2024Identifying Shopping Intent in Product QA for Proactive Recommendations
  7. 2026Advancing Model Research in AgentX: Long-Horizon Autonomy for Industrial Recommender Systems
  8. 2026QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation