Traditional signature- and rule-based cybersecurity detection methods inadequately address novel attacks, zero-day vulnerabilities, and sophisticated attack chains. We propose an LLM-driven enhancement framework integrating retrieval-augmented generation (RAG) techniques with agent-based architectures to automate vulnerability detection and response processes. Our approach employs multi-stage task decomposition mechanisms and leverages Monte Carlo Tree Search (MCTS) to refine agent reasoning paths. Experimental validation demonstrates a 340% improvement in zero-shot success rates within WebShop simulated environments, while achieving 95.4% success rates in online search tasks during Capture The Flag (CTF) competitions, substantially outperforming conventional reinforcement learning baselines. These findings establish that integrating LLMs with RAG techniques and agent-based architectures significantly enhances intelligent decision-making capabilities in complex cybersecurity scenarios, providing a novel paradigm for adaptive threat response.