SAGE: A Service Agent Graph-guided Evaluation Benchmark
Researchers introduce SAGE, a multi-agent benchmark using dynamic dialogue graphs to evaluate LLM compliance with structured procedures. Experiments reveal a significant 'Execution Gap' where models correctly identify user intent but fail to take the appropriate next steps.
Computer Science > Artificial Intelligence
Title:SAGE: A Service Agent Graph-guided Evaluation Benchmark
View PDF HTML (experimental)Abstract:The development of Large Language Models (LLMs) has catalyzed automation in customer service, yet benchmarking their performance remains challenging. Existing benchmarks predominantly rely on static paradigms and single-dimensional metrics, failing to account for diverse user behaviors or the strict adherence to structured Standard Operating Procedures (SOPs) required in real-world deployments. To bridge this gap, we propose SAGE (Service Agent Graph-guided Evaluation), a universal multi-agent benchmark for automated, dual-axis assessment. SAGE formalizes unstructured SOPs into Dynamic Dialogue Graphs, enabling precise verification of logical compliance and comprehensive path coverage. We introduce an Adversarial Intent Taxonomy and a modular Extension Mechanism, enabling low-cost deployment across domains and facilitating automated dialogue data synthesis. Evaluation is conducted via a framework where Judge Agents and a Rule Engine analyze interactions between User and Service Agents to generate deterministic ground truth. Extensive experiments on 27 LLMs across 6 industrial scenarios reveal a significant Execution Gap'' where models accurately classify intents but fail to derive correct subsequent actions. We also observe Empathy Resilience'', a phenomenon where models maintain polite conversational facades despite underlying logical failures under high adversarial intensity. Code and resources are available at this https URL.
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Source: arXiv cs.AI Recent
