# CRMArena-Pro: Holistic Assessment of LLM Agents Across Diverse Business Scenarios and Interactions

arXiv: https://arxiv.org/abs/2505.18878 · HTML: https://mlsanity.com/paper/2505.18878/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 21 references that are in the map.
- First posted on arXiv 24 May 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 17 of 17)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions](https://mlsanity.com/paper/2404.13208/) (2024)
- [WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?](https://mlsanity.com/paper/2403.07718/) (2024)
- [TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks](https://mlsanity.com/paper/2412.14161/) (2024)
- [APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay](https://mlsanity.com/paper/2504.03601/) (2025)
- [SafeArena: Evaluating the Safety of Autonomous Web Agents](https://mlsanity.com/paper/2503.04957/) (2025)
- [WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks](https://mlsanity.com/paper/2407.05291/) (2024)
- [CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments](https://mlsanity.com/paper/2411.02305/) (2024)
- [WorkBench: a Benchmark Dataset for Agents in a Realistic Workplace Setting](https://mlsanity.com/paper/2405.00823/) (2024)
- [Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles](https://mlsanity.com/paper/2309.09369/) (2023)
- [Evaluating Cultural and Social Awareness of LLM Web Agents](https://mlsanity.com/paper/2410.23252/) (2024)
- [SafeWorld: Geo-Diverse Safety Alignment](https://mlsanity.com/paper/2412.06483/) (2024)
- [Multimodal Cultural Safety: Evaluation Framework and Alignment Strategies](https://mlsanity.com/paper/2505.14972/) (2025)

## Built on by (most cited first, 30 of 47)

- [Magentic-UI: Towards Human-in-the-loop Agentic Systems](https://mlsanity.com/paper/2507.22358/) (2025)
- [ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces](https://mlsanity.com/paper/2604.05172/) (2026)
- [Workspace-Bench 1.0: Benchmarking AI Agents on Workspace Tasks with Large-Scale File Dependencies](https://mlsanity.com/paper/2605.03596/) (2026)
- [Benchmarking Deep Search over Heterogeneous Enterprise Data](https://mlsanity.com/paper/2506.23139/) (2025)
- [Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics](https://mlsanity.com/paper/2510.17797/) (2025)
- [RecoWorld: Building Simulated Environments for Agentic Recommender Systems](https://mlsanity.com/paper/2509.10397/) (2025)
- [AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance](https://mlsanity.com/paper/2506.03828/) (2025)
- [EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings](https://mlsanity.com/paper/2603.13594/) (2026)
- [AgentGuardian: Learning Access Control Policies to Govern AI Agent Behavior](https://mlsanity.com/paper/2601.10440/) (2026)
- [World of Workflows: A Benchmark for Bringing World Models to Enterprise Systems](https://mlsanity.com/paper/2601.22130/) (2026)
- [Echoing: Identity Failures when LLM Agents Talk to Each Other](https://mlsanity.com/paper/2511.09710/) (2025)
- [AgentArch: A Comprehensive Benchmark to Evaluate Agent Architectures in Enterprise](https://mlsanity.com/paper/2509.10769/) (2025)
- [Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application](https://mlsanity.com/paper/2606.12191/) (2026)
- [SCUBA: Salesforce Computer Use Benchmark](https://mlsanity.com/paper/2509.26506/) (2025)
- [Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation](https://mlsanity.com/paper/2606.10749/) (2026)
- [SAGE: A Top-Down Bottom-Up Knowledge-Grounded User Simulator for Multi-turn AGent Evaluation](https://mlsanity.com/paper/2510.11997/) (2025)
- [Talk, Evaluate, Diagnose: User-aware Agent Evaluation with Automated Error Analysis](https://mlsanity.com/paper/2603.15483/) (2026)
- [CRMWeaver: Building Powerful Business Agent via Agentic RL and Shared Memories](https://mlsanity.com/paper/2510.25333/) (2025)
- [On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification](https://mlsanity.com/paper/2608.18066/) (2026)
- [UI-CUBE: Enterprise-Grade Computer Use Agent Benchmarking Beyond Task Accuracy to Operational Reliability](https://mlsanity.com/paper/2511.17131/) (2025)
- [One Success Isn't Reliability: Thinkingbox, a Sandbox and Benchmark for Agents in Stateful Business Workflows](https://mlsanity.com/paper/2608.19741/) (2026)
- [PolicyGuide: From Guarding One Action to Guiding the Whole Workflow for Policy-Compliant LLM Agents](https://mlsanity.com/paper/2608.19861/) (2026)
- [The Era by Eon Benchmark: A Generated Enterprise Estate with Exact Ground Truth for Benchmarking LLM Agents](https://mlsanity.com/paper/2609.09853/) (2026)
- [Don't Make Models Guess Security and Safety: Symbolic Guardrails for Domain-Specific AI Agents](https://mlsanity.com/paper/2604.15579/) (2026)
- [HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following](https://mlsanity.com/paper/2607.25398/) (2026)
- [ττ-Bench: An Environment for End-To-End, Realistic Agent Construction](https://mlsanity.com/paper/2609.04611/) (2026)
- [EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprises](https://mlsanity.com/paper/2603.21630/) (2026)
- [Rethinking Sales Lead Scoring with LLM-based Hierarchical Preference Ranking](https://mlsanity.com/paper/2606.04387/) (2026)
- [ERPBench: Evaluating LLM Agents for Enterprise Decision-Making Across Competitive Market Ecologies](https://mlsanity.com/paper/2609.04667/) (2026)
- [IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier](https://mlsanity.com/paper/2609.10494/) (2026)

## Abstract (arXiv)

While AI agents hold transformative potential in business, effective performance benchmarking is hindered by the scarcity of public, realistic business data on widely used platforms. Existing benchmarks often lack fidelity in their environments, data, and agent-user interactions, with limited coverage of diverse business scenarios and industries. To address these gaps, we introduce CRMArena-Pro, a novel benchmark for holistic, realistic assessment of LLM agents in diverse professional settings. CRMArena-Pro expands on CRMArena with nineteen expert-validated tasks across sales, service, and 'configure, price, and quote' processes, for both Business-to-Business and Business-to-Customer scenarios. It distinctively incorporates multi-turn interactions guided by diverse personas and robust confidentiality awareness assessments. Experiments reveal leading LLM agents achieve only around 58% single-turn success on CRMArena-Pro, with performance dropping significantly to approximately 35% in multi-turn settings. While Workflow Execution proves more tractable for top agents (over 83% single-turn success), other evaluated business skills present greater challenges. Furthermore, agents exhibit near-zero inherent confidentiality awareness; though targeted prompting can improve this, it often compromises task performance. These findings highlight a substantial gap between current LLM capabilities and enterprise demands, underscoring the need for advancements in multi-turn reasoning, confidentiality adherence, and versatile skill acquisition.