Integrating LLMs into Autonomous Systems and Critical Decision Support Systems

This Habr article explores the evolution of LLMs from simple chatbots into active agents capable of interacting with tools and making decisions in real-world systems. The authors analyze seven key studies focused on multi-agent architectures, model orchestration, and AI action control methods. The main takeaway is that the reliability of autonomous systems today is determined less by the power of an individual model and more by the quality of the surrounding architecture. The material details strategies for separating planning from execution, the importance of boundary validation, and the role of humans in the control loop. Experts emphasize that using multiple models is not always the optimal solution and highlight the necessity of AI confidence calibration. This article is useful for developers and engineers working on implementing autonomous intelligent systems into critical business processes where the cost of failure is extremely high.