To address the challenge of enhancing both accuracy and fine-grained decision-making in adversarial settings involving AI agents powered by large language models (LLMs), this study introduces a novel strategy generation approach that integrates expert policies with multi-chain-of-thought (CoT) reasoning. By fusing real-time visual input (images) with structured observational data (text), the method provides LLMs with richer situational awareness. Time-sensitive expert strategies and parallel reasoning chains are embedded into prompt design, significantly improving the agent’s control and tactical precision. Experimental validation in high-difficulty StarCraft II scenarios demonstrates that the method achieves a 95% win rate without any additional model training. Results indicate that the approach enables interpretable and fine-tuned decision outputs in highly dynamic adversarial environments, offering a compelling pathway for leveraging LLMs in strategic behavior generation under competitive conditions.