Continuous Benchmark Generation for Evaluating Enterprise-scale LLM Agents

• URL: http://arxiv.org/abs/2511.10049v1
• Date: Fri, 14 Nov 2025 01:28:45 GMT
• Title: Continuous Benchmark Generation for Evaluating Enterprise-scale LLM Agents
• Authors: Divyanshu Saxena, Rishikesh Maurya, Xiaoxuan Ou, Gagan Somashekar, Shachee Mishra Gupta, Arun Iyer, Yu Kang, Chetan Bansal, Aditya Akella, Saravan Rajmohan,
• Abstract summary: We propose a process of benchmark generation that helps evolve the benchmarks as the requirements change and perform robust evaluation of evolving AI agents.<n>Our approach relies on semi-structured documents where developers express the high-level intent, and uses state-of-the-art LLMs to generate benchmarks from just a small number of such documents.
• Score: 23.277131100190086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid adoption of AI agents across domains has made systematic evaluation crucial for ensuring their usefulness and successful production deployment. Evaluation of AI agents typically involves using a fixed set of benchmarks and computing multiple evaluation metrics for the agent. While sufficient for simple coding tasks, these benchmarks fall short for enterprise-scale agents, where services and requirements evolve continuously and ground-truth examples are sparse. We propose a process of benchmark generation that helps evolve the benchmarks as the requirements change and perform robust evaluation of evolving AI agents. We instantiate this approach for a case study of service migration from one deployment platform to another at a large public enterprise. Our approach relies on semi-structured documents where developers express the high-level intent, and uses state-of-the-art LLMs to generate benchmarks from just a small number of such documents. Overall, this process results in a maintainable evaluation framework, enabling rapid feedback on agent performance and facilitating targeted improvements.

Related papers

• AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress [71.02263260394261]
  Large language models (LLMs) still encounter challenges in multi-turn decision-making tasks.<n>We build process reward models (PRMs) to evaluate each decision and guide the agent's decision-making process.<n>AgentPRM captures both the interdependence between sequential decisions and their contribution to the final goal.
  arXiv  Detail & Related papers  (2025-11-11T14:57:54Z)
• Towards Outcome-Oriented, Task-Agnostic Evaluation of AI Agents [1.0305173936249623]
  This white paper proposes a novel framework of eleven outcome-based, task-agnostic performance metrics for AI agents.<n>We introduce metrics such as Goal Completion Rate (GCR), Autonomy Index (AIx), Multi-Step Task Resilience (MTR), and Business Impact Efficiency (BIE)<n>Our results reveal significant performance trade-offs between different agent designs, highlighting the Hybrid Agent as the most consistently high-performing model.
  arXiv  Detail & Related papers  (2025-11-11T13:40:46Z)
• Automatically Benchmarking LLM Code Agents through Agent-Driven Annotation and Evaluation [47.85891728056131]
  PRDBench is a novel benchmark comprising 50 real-world Python projects across 20 domains, each with structured Product Requirement Document (PRD) requirements, comprehensive evaluation criteria, and reference implementations.<n>We employ an Agent-as-a-Judge paradigm to score agent outputs, enabling the evaluation of various test types beyond unit tests.
  arXiv  Detail & Related papers  (2025-10-28T12:26:45Z)
• From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Production [6.189323683437766]
  This paper reports IBM's experience developing and piloting the Computer Using Generalist Agent (CUGA)<n>CUGA adopts a hierarchical planner--executor architecture with strong analytical foundations.<n>It was evaluated in a pilot within the Business-Process-Outsourcing talent acquisition domain.
  arXiv  Detail & Related papers  (2025-10-27T20:55:00Z)
• FreshBrew: A Benchmark for Evaluating AI Agents on Java Code Migration [2.981397088242044]
  We introduce FreshBrew, a novel benchmark for evaluating AI agents on project-level Java migrations.<n>We benchmark several state-of-the-art LLMs, and compare their performance against established rule-based tools.<n>Our evaluation of AI agents on this benchmark of 228 repositories shows that the top-performing model, 2.5 Gemini Flash, can successfully migrate 52.3 percent of projects to 17.
  arXiv  Detail & Related papers  (2025-10-06T14:39:58Z)
• MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models [76.72220653705679]
  We introduce MCPEval, an open-source framework that automates end-to-end task generation and deep evaluation of intelligent agents.<n> MCPEval standardizes metrics, seamlessly integrates with native agent tools, and eliminates manual effort in building evaluation pipelines.<n> Empirical results across five real-world domains show its effectiveness in revealing nuanced, domain-specific performance.
  arXiv  Detail & Related papers  (2025-07-17T05:46:27Z)
• Establishing Best Practices for Building Rigorous Agentic Benchmarks [94.69724201080155]
  We show that many agentic benchmarks have issues in task setup or reward design.<n>Such issues can lead to under- or overestimation of agents' performance by up to 100% in relative terms.<n>We introduce the Agentic Benchmark Checklist (ABC), a set of guidelines that we synthesized from our benchmark-building experience.
  arXiv  Detail & Related papers  (2025-07-03T17:35:31Z)
• BenchAgents: Multi-Agent Systems for Structured Benchmark Creation [23.653678381444276]
  BenchAgents is a framework that automates the creation of evaluation benchmarks.<n>We use BenchAgents to create benchmarks to evaluate capabilities related to planning, constraint satisfaction, and causal reasoning.<n>We then use these benchmarks to study state-of-the-art models and extract new insights into common failure modes and model differences.
  arXiv  Detail & Related papers  (2024-10-29T22:56:18Z)
• Agent-as-a-Judge: Evaluate Agents with Agents [61.33974108405561]
  We introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We present DevAI, a new benchmark of 55 realistic automated AI development tasks.
  arXiv  Detail & Related papers  (2024-10-14T17:57:02Z)
• AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents [74.16170899755281]
  We introduce AgentBoard, a pioneering comprehensive benchmark and accompanied open-source evaluation framework tailored to analytical evaluation of LLM agents.<n>AgentBoard offers a fine-grained progress rate metric that captures incremental advancements as well as a comprehensive evaluation toolkit.<n>This not only sheds light on the capabilities and limitations of LLM agents but also propels the interpretability of their performance to the forefront.
  arXiv  Detail & Related papers  (2024-01-24T01:51:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.