WISE-Flow: Workflow-Induced Structured Experience for Self-Evolving Conversational Service Agents

• URL: http://arxiv.org/abs/2601.08158v1
• Date: Tue, 13 Jan 2026 02:43:41 GMT
• Title: WISE-Flow: Workflow-Induced Structured Experience for Self-Evolving Conversational Service Agents
• Authors: Yuqing Zhou, Zhuoer Wang, Jie Yuan, Hong Wang, Samson Koelle, Ziwei Zhu, Wei Niu,
• Abstract summary: Large language model (LLM)-based agents are widely deployed in user-facing services but remain error-prone in new tasks.<n>We propose WISE-Flow, a feasibility-centric framework that converts historical service interactions into reusable procedural experience.
• Score: 12.014029662322152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM)-based agents are widely deployed in user-facing services but remain error-prone in new tasks, tend to repeat the same failure patterns, and show substantial run-to-run variability. Fixing failures via environment-specific training or manual patching is costly and hard to scale. To enable self-evolving agents in user-facing service environments, we propose WISE-Flow, a workflow-centric framework that converts historical service interactions into reusable procedural experience by inducing workflows with prerequisite-augmented action blocks. At deployment, WISE-Flow aligns the agent's execution trajectory to retrieved workflows and performs prerequisite-aware feasibility reasoning to achieve state-grounded next actions. Experiments on ToolSandbox and $τ^2$-bench show consistent improvement across base models.

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