Engineering Trustworthy Autonomous AI Agents: Architectural Patterns for Self-Planning, Tool-Oriented Reasoning, and Enterprise Task Completion

Agentic AI is revolutionizing Enterprise systems and moving them from being reactive Cloud powered Chatbots and into being autonomous systems that are capable of reasoning, planning and actions. In this study, a robust and reliable architecture for an AI agent system is proposed, featuring components such as self-planning, tool-oriented reasoning, memory management, and governance within an enterprise for workflow automation. A synthetic dataset with 5,000 enterprise task records has been analyzed in order to reach the desired level of accuracy of 99.8% on the classification task through the Logistic Regression model, and it has a ROC-AUC of 99.3% on the enterprise task score. Through feature analysis the most important feature that influenced task success was identified as planning depth. The results show that scalable, explainable, and secure agent systems can improve autonomous decision-making processes and even boost the efficiency of enterprises' operations.