<p class="MsoNormal" style="margin-bottom: 12.0pt; text-align: left; mso-line-height-alt: 12.0pt; layout-grid-mode: char; mso-layout-grid-align: none;" align="left">Evaluating Autonomous Computational Agents for Complex Logic and Security-Oriented Task Environments

This study presents an analytical evaluation of autonomous computational agents designed to solve complex, logicintensive challenges in dynamic digital environments. The framework explores how reasoning-based algorithms, adaptive learning mechanisms, and multi-step decision strategies can be employed to achieve goal-oriented automation without domainspecific pretraining. Through systematic experimentation, the research examines agentic behavior under variable task conditions, emphasizing adaptability, robustness, and problem decomposition efficiency. Empirical analysis demonstrates that structured reasoning and iterative self-correction can enhance performance consistency across diverse computational tasks. The findings contribute to advancing intelligent agent architectures in computer science, offering insights into scalable, general-purpose systems for autonomous decision-making and digital security problem solving.