Creative Adversarial Testing (CAT): A Novel Framework for Evaluating Goal-Oriented Agentic AI Systems
This addresses a critical gap in evaluating Agentic AI systems for developers and researchers, though it appears incremental as it builds on existing evaluation techniques.
The paper tackles the problem of evaluating goal-task alignment in Agentic AI systems, introducing the Creative Adversarial Testing (CAT) framework and validating it with synthetic data from Alexa+ audio services to provide insights for optimization.
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.