IEL: Intent-Level Experience Learning for LLM-based Customer Service Agents

Large language model agents increasingly operate in interactive service environments where a single dialogue may contain multiple user intents with different outcomes. This creates a granularity mismatch for experience learning: episode-level success or failure cannot determine which intent-specific behaviors should be retained, discarded, or reused. We introduce Intent-level Experience Learning (IEL), which treats a cross-turn intent instance as the unit of experience attribution and procedural learning. IEL first reconstructs intent instances from multi-turn interactions, attributes dialogue and tool evidence to individual instances, and verifies their outcomes using intent-specific goals and completion criteria. It then induces reusable procedures from verified successful instances, while using verified failures, when available, to identify recurring error patterns. Taxonomy-absent intents are accumulated as candidates and may subsequently be merged with existing intents or promoted to new canonical intents as evidence grows. This design implements the principle of credit assignment before procedural abstraction, where credit assignment refers to associating interaction evidence and outcomes with individual intent instances rather than with the entire episode. We evaluate IEL on ECom-Bench and a cross-category extension covering previously unseen product categories, using three executor LLMs. IEL obtains the highest mean score in all evaluated executor–metric combinations, with improvements of up to 7.58 percentage points in pass$\text{textasciicircum}$1 and 13.18 points in pass$\text{textasciicircum}$3 in the in-domain setting. These results suggest that intent-level experience attribution provides a useful basis for learning reusable procedures from mixed-outcome service interactions.