This paper introduces an innovative approach to task completion analysis in multi-agent systems using large language models (LLMs) [3]. Unlike traditional methods, which rely on binary indicators of task completion, our approach emphasizes iterative, context-driven task distribution and execution [7]. Tasks are dynamically redistributed by the Centralized Control Unit (CCU), which operates as a coordinator rather than a final decision-maker on task completion. The thematic context of the first incoming task serves as a guiding framework, allowing agents to iteratively refine their operations and share progress through language-based outputs [1]. The system leverages LLM-generated insights to assess task states, recommend further actions, and dynamically adapt task assignments. Task completion is not determined by static metrics but through linguistic confirmations and iterative feedback loops. This adaptive framework ensures unresolved tasks continue to circulate until a comprehensive resolution is achieved, enabling seamless management of evolving and interdependent tasks in multi-agent environments. The proposed approach offers significant advantages in dynamic environments by fostering adaptability, mitigating bottlenecks, and providing a scalable solution for managing complex, context-dependent tasks across various domains.

Modern multi-agent systems (MAS) often rely on static task allocation methods, which are insufficient for dynamic environments requiring continuous task reassignment and optimization [10]. This paper introduces a theoretical framework that integrates fine-tuned large language models (LLMs) into MAS to enable semantic task evaluation and dynamic task distribution [15]. By analyzing language-based task states, the system identifies hidden dependencies and adapts task allocation in real time [12]. While the proposed system is in the conceptual phase, it offers a promising solution for automating complex workflows, reducing human intervention, and improving scalability in dynamic environments. Future work will focus on prototyping and validating the framework in real-world scenarios [1].