The Large Language Models (LLMs) provides excellent natural language understanding, multi-step reasoning and content creation capabilities. For example, for complex decision making problems, conventional single agent systems have limitations that are intrinsic for the system where everything is done within a single structural constraint planning, memory management, execution and verification. This results in hallucination, failure to keep context in memory at a higher time-scale, failure to decompose task and inadequate adaptability in dynamic environment. The idea is introduced in this paper of the Multi-Agent LLM Framework for Autonomous Decision Systems (MALF-ADS), which fragments the cognitive domains into 4 specialized agents (Planner Agent, Memory Agent, Execution Agent, Evaluation Agent). Agents work independently of each other, have a clear assignment and communicate via structured coordination layer. Incoming tasks are broken down into atomic subtasks by the Planner Agent, persistent contextual knowledge stored in a vector database as part of the Memory Agent, data processed by invoking tools and executing them in the Execution Agent, and validated outputs and computed confidence scores in the Evaluation Agent. The communication protocol in the architecture is dynamic and allows agents to communicate by passing messages which are context-aware. MALF-ADS outperforms all other single LLM, RL agent, and memory-augmented baselines on a 50,000-sample multi-domain dataset in terms of decision accuracy (97%), task success rate (95%), and mean response time (1.3?s).
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