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OutboundEval: A Dual-Dimensional Benchmark for Expert ...
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With the pervasive integration of Large Language Models (LLMs) across various industries, AI-driven automated outbound calling is emerging as a critical component for enterprises to optimize customer communication and enhance operational efficiency (Wen et al., 2025; Kaewtawee et al., 2025; Kaiyrbekov et al., 2025; Lang and Eskenazi, 2025). Its applications span a wide range of domains, including recruitment, market research, sales, and customer service, with several benchmarks—such as Xbench (Chen et al., 2025)—having established evaluation criteria for them. However, a standardized benchmark specifically designed for outbound calling scenarios is currently lacking to comprehensively and objectively evaluate the performance of these models in real-world tasks. Existing evaluation efforts predominantly focus on general conversational abilities or single-turn instruction following, and suffer from insufficient dataset volume and category coverage, unrealistic user simulation, and inaccurate or unreasonable evaluation metrics. “This framework assesses the capabilities of outbound calling agents from three primary dimensions: benchmark development, user simulator, and evaluation methodology.” • Benchmark Development: We have constructed a comprehensive, scenario-based corpus derived from authentic outbound calling business data. This corpus encompasses six major business domains and 30 representative sub-scenarios. For each sub-scenario, we have established a detailed evaluation scheme that includes scenario-specific process decomposition, a weighted scoring system, and domain-adaptive metrics, forming a solid foundation for nuanced and objective assessment.
To address this gap, we introduce OutboundEval, an evaluation framework designed to drive the advancement of outbound calling AI towards greater intelligence, human-like interaction, and efficiency. This framework assesses the capabilities of outbound calling agents from three primary dimensions: benchmark development, user simulator, and evaluation methodology. Key features of this framework include: “For each sub-scenario, we have established a detailed evaluation scheme that includes scenario-specific process decomposition, a weighted scoring system, and domain-adaptive metrics, forming a solid foundation for nuanced and objective assessment.” • User Simulator: To facilitate scalable and consistent evaluation, we propose a systematic process for constructing user simulators. By leveraging interaction data from real-world business scenarios, we build a large number of effective and stable user simulators. This allows for the testing of models in a controlled and reproducible environment, examining their task completion capabilities across various communication styles.
Language-model-driven simulators have been used to generate interactive agents across settings, including non-player characters in text games (Kim et al., 2022), multi-agent social environments (Wu et al., 2024; Park et al., 2023), and human–AI interaction for online shopping or web search (Chen et al., 2024a; Zhang et al., 2024). τ\tau-bench is the first to deploy LM role simulators for automated agent reliability testing, focusing on retail and airline customer service and demonstrating the feasibility and value of simulation-based evaluation (Yao et al., 2024). Yet prior simulators rarely couple dialogue evaluation with spoken output assessment, and few are grounded in outbound calling workflows. “These role simulations create a controllable and reproducible environment for testing agents, enabling systematic evaluation of their performance under different communication styles.” 3Benchmark Development
Language-model-driven simulators have been used to generate interactive agents across settings, including non-player characters in text games (Kim et al., 2022), multi-agent social environments (Wu et al., 2024; Park et al., 2023), and human–AI interaction for online shopping or web search (Chen et al., 2024a; Zhang et al., 2024). τ\tau-bench is the first to deploy LM role simulators for automated agent reliability testing, focusing on retail and airline customer service and demonstrating the feasibility and value of simulation-based evaluation (Yao et al., 2024). Yet prior simulators rarely couple dialogue evaluation with spoken output assessment, and few are grounded in outbound calling workflows. “These role simulations create a controllable and reproducible environment for testing agents, enabling systematic evaluation of their performance under different communication styles.” 3Benchmark Development
• Role: Definition of the user’s identity. “Background: Demographics, key experiences, personality traits, and pre-call context.” • Core Concerns: Ranked list of primary, secondary, and latent user concerns.
Dimension Element Description and Example Basic Information Belonging Scenario Core business scenario for evaluation, example: Rider recruitment Background Setting Demographic Characteristics Occupation, age, etc., affecting language habits and needs, example: 21 years old, college student Current Situation Why they become the target of outbound calls, affecting their initial willingness, example: Browsed job websites, has a need for part-time work Knowledge Level Degree of understanding of the business, determining the depth of their questions, example: Knows about rider work but doesn’t understand specific salary structure Personality and Behavior Core Personality Main personality of the profile, example: Cautious, impatient, talkative Communication Style Dialogue characteristics, example: Uses short sentences, tends to digress, polite/direct Behavioral Motivation Their intrinsic needs and concerns, example: Pursues cost-effectiveness, worries about being deceived, values time Dialogue Strategy Core Task (Must Ask) Information points that simulated users must know, used to test the outbound AI’s information provision ability. Example: Must ask "How much per order?" Preset Obstacles (Refusal/Hesitation Scripts) Standard scripts users use at specific points (e.g., when asked for phone number). Example: "Hmm, maybe next month." Key Triggers Defines positive/negative emotion triggers for the profile. Example: Negative trigger - AI speaks mechanically; Positive trigger - AI proactively provides key information. Cooperation Conditions (Provide Information) Conditions under which simulated users choose to cooperate and achieve the outbound AI’s goal. Example: Provide phone number after all core questions are satisfactorily answered. Hang-up Conditions (Refuse Communication) Conditions under which simulated users choose to actively end the call. Example: AI avoids core questions twice in a row. “End-to-End Responding Latency” Topic: Customer Service Complaint Handling Scenario Through proactive outbound calls, we thoroughly understand the specific issues and genuine demands of complaining customers, provide professional and feasible solutions, and establish a sound follow-up mechanism to restore customer satisfaction. Task Details Step 1: Identity Verification and Opening Greeting (Duration: 1-2 minutes) Standard Script: Hello, may I speak with Mr. Zhang Qiang? I am Li Ming, a customer service specialist at Changjiang Bank, employee ID 88888. Regarding the account fee deduction complaint you submitted on January 1st, we take it very seriously. Would now be convenient for a detailed 5–10 minute discussion? Step 2: Express Importance and Establish Initial Trust (Duration: 30 seconds–1 minute) Standard Script: Mr. Zhang, first, I apologize for the trouble this has caused you. As a valued customer of ours, the bank’s leadership has specifically assigned me to handle your issue. I will follow up thoroughly until you are completely satisfied. Step 3: Problem Detail Collection (Duration: 3–5 minutes) Guiding Script: Could you please describe the specific situation in detail? For example, when did you notice the fee deduction? Which card was involved? What transaction were you conducting at the time? In-Depth Understanding: What impact has this 200 RMB fee deduction had on you? How would you like us to address it? Key Points to Record: Time, amount, type of transaction, customer’s loss, handling expectations Step 4: Problem Analysis and Preliminary Judgment (Duration: 1–2 minutes) Professional Response: Based on your description, this may involve ××× fees. I will immediately check the relevant transaction records and basis for the fee deduction for you. Reassurance Script: Please rest assured that if this is indeed an issue on our end, we will certainly take responsibility and provide you with a satisfactory solution.