Operational AGI: A Language-Based Approach for Adaptive Multi-Agent Systems
Raman Marozau · 2025
This paper presents a multi-agent, NLP-driven task coordination framework that constitutes a practical architecture for Artificial General Intelligence (AGI). It leverages real-time agent-driven analysis, language-based task structuring, and LLM-powered iterative optimization to enhance adaptability in dynamic environments. Traditional static evaluation methods in multi-agent systems struggle with evolving task constraints and interdependencies, often leading to inefficiencies in decision-making and workload distribution. While existing multi-agent architectures have introduced task-sharing mechanisms, they remain rigid in dynamic execution scenarios, lacking real-time adaptation to agent constraints and evolving system states. To address these challenges, we introduce a hierarchical task allocation model that integrates a Centralized Control Unit (CCU) for system-wide orchestration, an NLP-based Dynamic Task Allocation (NLP DTA) module for context-aware task structuring, and distributed agents that iteratively refine execution through structured feedback loops. Unlike existing multi-agent coordination models, which rely on preprogrammed heuristics or static task allocation, our AGI framework enables agents to first self-assess task feasibility, allowing NLP DTA to construct an optimal execution plan. This enables iterative task reassignment, where LLM-driven analysis dynamically adjusts agent responsibilities based on real-time execution feedback. The proposed AGI architecture demonstrates strong scalability and adaptability, making it suitable for AI-driven applications such as autonomous cloud resource allocation, workflow optimization, and decentralized automation. By replacing rigid task evaluation mechanisms with an adaptive, context-aware approach, this work enhances adaptive coordination in multi-agent systems, providing a scalable and computationally efficient path toward embodied AGI in dynamic AI environments. Our findings highlight new directions for optimizing real-time task evaluation in language-based AI models, distributed systems, and AGI-driven decision support.