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arXiv · 2607.18039

Evidence-in-the-Loop: Trace-Driven Optimization for Customer-Service LLM Agents

Chunming Wu·Dafei Qiu·Congde Yuan·Charles Quan·Jun Wu·Suipeng Li·Mo Wu·Gavin Xie·Hope Chen·Max Yao
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Abstract

Production customer-service bots must improve answer quality across iterative releases, yet large language models must not bypass evidence boundaries, policy rules, or human-handoff safeguards. We present an \textbf{Evidence-Grounded Customer-Service Agent Workflow} deployed in a real-world customer-service setting. BM25 recall, issue-title-vector recall, issue-description-vector recall, weighted RRF fusion, and cross-encoder reranking construct grounded FAQ evidence for controlled LLM decisions. Policy-guided orchestration then combines this RAG evidence with scenario-specific rule evidence, conversation memory, and clarification state inside a fixed LangGraph DAG~\cite{langgraph2024}. The paper contributes three reusable deployment patterns: \textbf{hybrid RAG evidence construction}, where multi-channel retrieval and reranking produce auditable FAQ candidates; \textbf{evidence-grounded issue/action decision}, where an Evidence-Grounded Decision Module selects an issue/action from typed FAQ evidence and scenario-specific rule evidence; and \textbf{trace-driven RAG and reranker improvement}, where traces diagnose whether failures come from recall, ranking, final candidate selection, clarification, rule-derived evidence, or action policy, and where reranker fine-tuning is evaluated not only for in-domain gain but also for forgetting risk.

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Chunming Wu, Dafei Qiu, Congde Yuan, Charles Quan, Jun Wu, Suipeng Li, Mo Wu, Gavin Xie, Hope Chen, Max Yao. 2026-07-20. Evidence-in-the-Loop: Trace-Driven Optimization for Customer-Service LLM Agents. https://arxiv.org/abs/2607.18039

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