This article details how Senscape's intelligent investment research assistant, FinGPT Agent, has upgraded its core model to Kimi K2 Thinking. This upgrade addresses the complex and high-intensity information processing demands within the financial investment research sector. FinGPT Agent, which serves tens of thousands of financial investment professionals monthly, faces key challenges including the stability of tool calling in intricate research scenarios, multi-tool collaboration, and the comprehension of complex instructions. The article highlights Kimi K2 Thinking model's exceptional performance in multi-step reasoning stability (achieving 300 steps of tool calling), collaboration across multiple tools (22 investment research tools), and complex instruction understanding. It is presented as currently the only domestic model capable of consistently meeting these demanding requirements while reducing operational costs by 60%. Furthermore, FinGPT Agent integrates Senscape's proprietary, high-value investment research database, accumulated over many years, granting it in-depth analytical capabilities that surpass general AI. Through practical case studies such as macro policy paradigm analysis, industry turning point research, and company deconstruction, the article demonstrates FinGPT Agent's advantage — powered by the new model — in rapidly extracting core conclusions from vast amounts of data, thereby significantly enhancing investment research efficiency.