Adaptive Meta-Agent: A Self-Optimizing Runtime Architecture for Autonomous LLM Agent Systems

Large Language Model (LLM)-based autonomous agents have transformed the way artificial intelligence systems solve complex tasks through planning, reasoning, and tool utilization. Despite recent advances in agentic AI frameworks, current systems typically operate with static configurations where model selection, prompt design, tool orchestration, and resource allocation are predefined by developers. These static architectures struggle to adapt when task complexity, execution failures, or resource constraints change during runtime. This paper proposes the Adaptive Meta-Agent, a runtime self-optimization architecture that enables autonomous AI agents to continuously evaluate their own execution, diagnose performance bottlenecks, and recommend or perform architectural adaptations without human intervention. Unlike existing agent frameworks that focus primarily on planning and execution, the proposed architecture introduces a meta-reasoning layer capable of analyzing model suitability, prompt effectiveness, tool performance, token utilization, memory consumption, and execution confidence. Based on runtime observations, the Adaptive Meta-Agent can dynamically switch language models, spawn specialized agents, optimize prompts, recommend alternative tools, compress contextual information, and improve future executions through persistent learning. The architecture aims to improve the robustness, efficiency, and adaptability of autonomous AI systems operating in enterprise environments. This paper presents the proposed architecture, runtime workflow, optimization strategies, and a research methodology for evaluating self-optimizing agent systems. Future work includes implementing the framework using modern agent orchestration platforms and benchmarking its performance across diverse enterprise AI tasks.