Evaluating The Autonomy Of AI Agents In Multi Stage Decision Making Processes
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Abstract
This study presents a comprehensive evaluation of the autonomy of AI agents engaged in multi-stage decision-making processes. We introduce a systematic framework that integrates performance metrics, adaptive learning assessments, and error recovery analysis to quantify the degree of independent operation across successive decision stages. By dissecting the iterative decision architecture and identifying critical evaluation parameters, our methodology offers insights into how AI agents adapt to dynamic environments and handle uncertainties. Empirical results demonstrate that robust autonomy evaluation not only enhances decision accuracy but also informs the development of resilient and flexible algorithmic strategies. The findings contribute to advancing the field of autonomous AI, providing a foundation for future research into improving the efficiency and reliability of multi-stage decision-making systems.
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