Evaluation and Incident Prevention in an Enterprise AI Assistant

• URL: http://arxiv.org/abs/2504.13924v1
• Date: Fri, 11 Apr 2025 20:10:04 GMT
• Title: Evaluation and Incident Prevention in an Enterprise AI Assistant
• Authors: Akash V. Maharaj, David Arbour, Daniel Lee, Uttaran Bhattacharya, Anup Rao, Austin Zane, Avi Feller, Kun Qian, Yunyao Li,
• Abstract summary: This paper presents a comprehensive framework for monitoring, benchmarking, and continuously improving such complex, multi-component systems under active development by multiple teams.<n>By adopting this holistic framework, organizations can systematically enhance the reliability and performance of their AI Assistants.
• Score: 20.635362734048723
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Enterprise AI Assistants are increasingly deployed in domains where accuracy is paramount, making each erroneous output a potentially significant incident. This paper presents a comprehensive framework for monitoring, benchmarking, and continuously improving such complex, multi-component systems under active development by multiple teams. Our approach encompasses three key elements: (1) a hierarchical ``severity'' framework for incident detection that identifies and categorizes errors while attributing component-specific error rates, facilitating targeted improvements; (2) a scalable and principled methodology for benchmark construction, evaluation, and deployment, designed to accommodate multiple development teams, mitigate overfitting risks, and assess the downstream impact of system modifications; and (3) a continual improvement strategy leveraging multidimensional evaluation, enabling the identification and implementation of diverse enhancement opportunities. By adopting this holistic framework, organizations can systematically enhance the reliability and performance of their AI Assistants, ensuring their efficacy in critical enterprise environments. We conclude by discussing how this multifaceted evaluation approach opens avenues for various classes of enhancements, paving the way for more robust and trustworthy AI systems.

Related papers

• Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications [0.0]
  This paper introduces a comprehensive framework for advancing multi-agent systems through Model Context Protocol (MCP) We extend previous work on AI agent architectures by developing a unified theoretical foundation, advanced context management techniques, and scalable coordination patterns. We identify current limitations, emerging research opportunities, and potential transformative applications across industries.
  arXiv  Detail & Related papers  (2025-04-26T03:43:03Z)
• Advancing Embodied Agent Security: From Safety Benchmarks to Input Moderation [52.83870601473094]
  Embodied agents exhibit immense potential across a multitude of domains. Existing research predominantly concentrates on the security of general large language models. This paper introduces a novel input moderation framework, meticulously designed to safeguard embodied agents.
  arXiv  Detail & Related papers  (2025-04-22T08:34:35Z)
• Beyond Black-Box Benchmarking: Observability, Analytics, and Optimization of Agentic Systems [1.415098516077151]
  The rise of agentic AI systems, where agents collaborate to perform diverse tasks, poses new challenges with observing, analyzing and optimizing their behavior.<n>Traditional evaluation and benchmarking approaches struggle to handle the non-deterministic, context-sensitive, and dynamic nature of these systems.<n>This paper explores key challenges and opportunities in analyzing and optimizing agentic systems across development, testing, and maintenance.
  arXiv  Detail & Related papers  (2025-03-09T20:02:04Z)
• Multi-Agent Risks from Advanced AI [90.74347101431474]
  Multi-agent systems of advanced AI pose novel and under-explored risks. We identify three key failure modes based on agents' incentives, as well as seven key risk factors. We highlight several important instances of each risk, as well as promising directions to help mitigate them.
  arXiv  Detail & Related papers  (2025-02-19T23:03:21Z)
• AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
  This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.<n>The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
  arXiv  Detail & Related papers  (2025-02-19T05:58:52Z)
• Table-Critic: A Multi-Agent Framework for Collaborative Criticism and Refinement in Table Reasoning [3.721438719967748]
  Table-Critic is a novel multi-agent framework that facilitates collaborative criticism and iterative refinement of the reasoning process.<n>Our framework consists of four specialized agents: a Judge for error identification, a Critic for comprehensive critiques, a Refiner for process improvement, and a Curator for pattern distillation.<n>Extensive experiments have demonstrated that Table-Critic achieves superior accuracy and error correction rates while maintaining computational efficiency and lower solution degradation rate.
  arXiv  Detail & Related papers  (2025-02-17T13:42:12Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose AOP, a novel framework for agent-oriented planning in multi-agent systems.<n>In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy.<n> Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• Investigating the Role of Instruction Variety and Task Difficulty in Robotic Manipulation Tasks [50.75902473813379]
  This work introduces a comprehensive evaluation framework that systematically examines the role of instructions and inputs in the generalisation abilities of such models. The proposed framework uncovers the resilience of multimodal models to extreme instruction perturbations and their vulnerability to observational changes.
  arXiv  Detail & Related papers  (2024-07-04T14:36:49Z)
• Dynamic Vulnerability Criticality Calculator for Industrial Control Systems [0.0]
  This paper introduces an innovative approach by proposing a dynamic vulnerability criticality calculator. Our methodology encompasses the analysis of environmental topology and the effectiveness of deployed security mechanisms. Our approach integrates these factors into a comprehensive Fuzzy Cognitive Map model, incorporating attack paths to holistically assess the overall vulnerability score.
  arXiv  Detail & Related papers  (2024-03-20T09:48:47Z)
• 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)
• ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models [65.79770974145983]
  ASSERT, Automated Safety Scenario Red Teaming, consists of three methods -- semantically aligned augmentation, target bootstrapping, and adversarial knowledge injection. We partition our prompts into four safety domains for a fine-grained analysis of how the domain affects model performance. We find statistically significant performance differences of up to 11% in absolute classification accuracy among semantically related scenarios and error rates of up to 19% absolute error in zero-shot adversarial settings.
  arXiv  Detail & Related papers  (2023-10-14T17:10:28Z)

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.