Creative Adversarial Testing (CAT): A Novel Framework for Evaluating Goal-Oriented Agentic AI Systems
- URL: http://arxiv.org/abs/2509.23006v1
- Date: Fri, 26 Sep 2025 23:52:20 GMT
- Title: Creative Adversarial Testing (CAT): A Novel Framework for Evaluating Goal-Oriented Agentic AI Systems
- Authors: Hassen Dhrif,
- Abstract summary: Creative Adversarial Testing (CAT) is a novel approach designed to capture and analyze the complex relationship between Agentic AI tasks and the system's intended objectives.<n>We validate the CAT framework through extensive simulation using synthetic interaction data modeled after Alexa+ audio services.<n>Our results demonstrate that the CAT framework provides unprecedented insights into goal-task alignment, enabling more effective optimization and development of Agentic AI systems.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Agentic AI represents a paradigm shift in enhancing the capabilities of generative AI models. While these systems demonstrate immense potential and power, current evaluation techniques primarily focus on assessing their efficacy in identifying appropriate agents, tools, and parameters. However, a critical gap exists in evaluating the alignment between an Agentic AI system's tasks and its overarching goals. This paper introduces the Creative Adversarial Testing (CAT) framework, a novel approach designed to capture and analyze the complex relationship between Agentic AI tasks and the system's intended objectives. We validate the CAT framework through extensive simulation using synthetic interaction data modeled after Alexa+ audio services, a sophisticated Agentic AI system that shapes the user experience for millions of users globally. This synthetic data approach enables comprehensive testing of edge cases and failure modes while protecting user privacy. Our results demonstrate that the CAT framework provides unprecedented insights into goal-task alignment, enabling more effective optimization and development of Agentic AI systems.
Related papers
- Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
arXiv Detail & Related papers (2026-01-25T12:47:25Z) - AI-NativeBench: An Open-Source White-Box Agentic Benchmark Suite for AI-Native Systems [52.65695508605237]
We introduce AI-NativeBench, the first application-centric and white-box AI-Native benchmark suite grounded in Model Context Protocol (MCP) and Agent-to-Agent (A2A) standards.<n>By treating agentic spans as first-class citizens within distributed traces, our methodology enables granular analysis of engineering characteristics beyond simple capabilities.<n>This work provides the first systematic evidence to guide the transition from measuring model capability to engineering reliable AI-Native systems.
arXiv Detail & Related papers (2026-01-14T11:32:07Z) - Agentic AI for Mobile Network RAN Management and Optimization [0.0]
Agentic AI represents a new paradigm for automating complex systems by using Large AI Models (LAMs)<n>This paper contributes to ongoing research on Agentic AI in 5G and 6G networks by tracing its evolution from classical agents to Agentic AI.<n>Core design patterns-reflection, planning, tool use, and multi-agent collaboration-are then described to illustrate how intelligent behaviors are orchestrated.
arXiv Detail & Related papers (2025-11-04T12:34:57Z) - From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions [70.72279728350763]
Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems.<n>Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and in response to environmental dynamics.<n>This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques.
arXiv Detail & Related papers (2025-10-07T05:45:25Z) - Benchmarking LLM-based Agents for Single-cell Omics Analysis [6.915378212190715]
AI agents offer a paradigm shift, enabling adaptive planning, executable code generation, traceable decisions, and real-time knowledge fusion.<n>We introduce a novel benchmarking evaluation system to rigorously assess agent capabilities in single-cell omics analysis.
arXiv Detail & Related papers (2025-08-16T04:26:18Z) - Agentic AI Frameworks: Architectures, Protocols, and Design Challenges [0.0]
Large Language Models have ushered in a transformative paradigm in artificial intelligence, where intelligent agents exhibit goal-directed autonomy, contextual reasoning, and dynamic multi-agent coordination.<n>This paper provides a systematic review and comparative analysis of leading Agentic AI frameworks, including CrewAI, LangGraph, AutoGen, Semantic Kernel, Agno, Google ADK, and MetaGPT.<n>We identify key limitations, emerging trends, and open challenges in the field.
arXiv Detail & Related papers (2025-08-13T19:16:18Z) - A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems [53.37728204835912]
Most existing AI systems rely on manually crafted configurations that remain static after deployment.<n>Recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback.<n>This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents.
arXiv Detail & Related papers (2025-08-10T16:07:32Z) - Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training [67.895981259683]
General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence.<n>Current agent systems are either closed-source or heavily reliant on a variety of paid APIs and proprietary tools.<n>We present Cognitive Kernel-Pro, a fully open-source and (to the maximum extent) free multi-module agent framework.
arXiv Detail & Related papers (2025-08-01T08:11:31Z) - Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting [48.131257144711576]
We focus on the application of AI agents to network troubleshooting.<n>We elaborate on the need for a standardized, reproducible, and open benchmarking platform.
arXiv Detail & Related papers (2025-07-01T08:46:37Z) - Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
arXiv Detail & Related papers (2025-06-22T16:52:48Z) - AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges [0.36868085124383626]
This study critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis.<n>Generative AI is positioned as a precursor, with AI Agents advancing through tool integration, prompt engineering, and reasoning enhancements.<n>Agentic AI systems represent a paradigmatic shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy.
arXiv Detail & Related papers (2025-05-15T16:21:33Z) - Leveraging Conversational Generative AI for Anomaly Detection in Digital Substations [0.0]
The research employs advanced performance metrics to conduct a comparative assessment between the proposed AD and HITL-based AD frameworks.<n>This approach presents a promising solution for enhancing the reliability of power system operations in the face of evolving cybersecurity challenges.
arXiv Detail & Related papers (2024-11-09T18:38:35Z)
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.