This study presents an analytical evaluation of autonomous computational agents designed to solve complex, logic-intensive 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 domain-specific 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.