Agentic AI systems use specialized agents to handle tasks within complex workflows, enabling automation and efficiency. However, optimizing these systems often requires labor-intensive, manual adjustments to refine roles, tasks, and interactions. This paper introduces a framework for autonomously optimizing Agentic AI solutions across industries, such as NLP-driven enterprise applications. The system employs agents for Refinement, Execution, Evaluation, Modification, and Documentation, leveraging iterative feedback loops powered by an LLM (Llama 3.2-3B). The framework achieves optimal performance without human input by autonomously generating and testing hypotheses to improve system configurations. This approach enhances scalability and adaptability, offering a robust solution for real-world applications in dynamic environments. Case studies across diverse domains illustrate the transformative impact of this framework, showcasing significant improvements in output quality, relevance, and actionability. All data for these case studies, including original and evolved agent codes, along with their outputs, are here: https://anonymous.4open.science/r/evolver-1D11/

翻译：智能体AI系统通过专用智能体处理复杂工作流中的任务，从而实现自动化与效率提升。然而，优化此类系统通常需要大量人工介入，以调整角色、任务及交互方式。本文提出一种跨行业自主优化智能体AI解决方案的框架，例如面向自然语言处理驱动的企业级应用。该系统部署了用于精炼、执行、评估、修改与文档记录的智能体，并利用基于LLM（Llama 3.2-3B）的迭代反馈环路。该框架通过自主生成并验证假设以改进系统配置，实现在无需人工干预下的性能最优化。此方法显著提升了系统的可扩展性与适应性，为动态环境中的实际应用提供了稳健解决方案。跨多个领域的案例研究证明了该框架的变革性影响，展现出在输出质量、相关性与可执行性方面的显著提升。所有案例研究数据，包括原始及演进后的智能体代码及其输出，均在此处：https://anonymous.4open.science/r/evolver-1D11/