VISTA: A Versatile Interactive User Simulation Toolkit for Agent Evaluation
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Abstract (arXiv)
Evaluation remains a critical bottleneck for interactive agent development. Existing evaluation methods often rely on static benchmarks, which fail to capture the dynamic, multi-step nature of agentic behavior and struggle to expose meaningful failure modes. While user-simulation-based evaluation offers a promising alternative, existing simulation frameworks suffer from two major limitations. First, they provide limited mechanisms for evaluating the quality and comprehensiveness of simulated interactions, making it difficult to assess whether a simulator sufficiently explores an agent's capabilities and failure modes. Second, most frameworks are restricted to either UI-only actions or API-only actions, limiting their ability to model the full range of realistic user behaviors. To address these limitations, we propose VISTA, a Versatile Interactive user Simulation Toolkit for Agent evaluation. Our toolkit includes a suite of six metrics for measuring the realism, capability coverage, and interaction effectiveness of simulated interactions. In addition, we develop a hybrid user simulator that integrates both UI-based interactions and API-based interactions, enabling more realistic and comprehensive evaluation across diverse interactive environments. We evaluate VISTA in e-commerce shopping and education customer service settings and demonstrate that it produces more realistic and comprehensive evaluations than existing methods.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 9 Jun 2026.
Builds on
18 of 25 references.
References · 18
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
- 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
- 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 627
- 2025Why Do Multi-Agent LLM Systems Fail?cited by 604
- 2025Survey on Evaluation of LLM-based Agentscited by 204
- 2025Evaluation and Benchmarking of LLM Agents: A Surveycited by 152
- 2025Where LLM Agents Fail and How They can Learn From Failurescited by 133
- 2024CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environmentscited by 71
- 2026Mind the Sim2Real Gap in User Simulation for Agentic Taskscited by 54
- 2025SimulatorArena: Are User Simulators Reliable Proxies for Multi-Turn Evaluation of AI Assistants?cited by 50
- 2025UXAgent: An LLM Agent-Based Usability Testing Framework for Web Designcited by 40
- 2026Lost in Simulation: LLM-Simulated Users are Unreliable Proxies for Human Users in Agentic Evaluationscited by 40
6 more
- 2025LLM Agent Meets Agentic AI: Can LLM Agents Simulate Customers to Evaluate Agentic-AI-based Shopping Assistants?cited by 15
- 2026RealUserSim: Bridging the Reality Gap in Agent Benchmarking via Grounded User Simulationcited by 12
- 2026Beyond Cooperative Simulators: Generating Realistic User Personas for Robust Evaluation of LLM Agentscited by 10
- 2025Human vs. Agent in Task-Oriented Conversationscited by 9
- 2025SAGE: A Top-Down Bottom-Up Knowledge-Grounded User Simulator for Multi-turn AGent Evaluationcited by 6
- 2026ShopGym: An Integrated Framework for Realistic Simulation and Scalable Benchmarking of E-Commerce Web Agentscited by 5
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Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026AppWorld-UL: Benchmarking Diverse Agent-User Interactions for Tool-Use
- 2025AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents
- 2026UserProxyBench: Evaluating LLM User Simulators for Agent Benchmarks and Training
- 2026AcCoRD: Evaluating User-Agent Collaboration Under Realistic User Preference Dynamics
- 2026Efficient Agent Evaluation via Diversity-Guided User Simulation
- 2026CRAB-Bench: Evaluating LLM Agents under Complex Task Dependencies and Human-aligned User Simulation
- 2026Iterating Toward Better Search: A Two-Agent Simulation Framework for Evaluating Agentic Search Architectures in E-Commerce
- 2026Interaction-as-a-Judge