Group 1 - The report introduces a paradigm shift in investment research through the integration of Deep Research technology, enabling autonomous planning capabilities in AI agents for investment research tasks [4][14]. - The system transforms investment research from "assisted search" to "end-to-end autonomous research," allowing for multi-round broad searches and cross-validation of information [4][14]. - The architecture employs a standardized three-layer decoupled structure with asynchronous parallel strategies, enhancing the efficiency of ESG task processing [5][19]. Group 2 - The deployment is cloud-native with zero-code interaction, allowing business personnel to customize research directions simply by adjusting natural language instructions [6][52]. - The production cycle of a single research report is compressed from hours to minutes, ensuring high-quality, standardized reports that can be replicated at scale [7][67]. - The system's design ensures consistency in data depth and formatting style, overcoming the quality fluctuations associated with manual delivery [67]. Group 3 - The report outlines a comprehensive ESG investment research architecture driven by Agentic AI, featuring a three-layer technical structure and dual-engine support [18][19]. - The system's core is designed to enhance research standardization and traceability, starting from precise user input to the delivery of structured JSON data alongside standardized Word reports [25][27]. - The architecture allows for dynamic loading of prompts and employs a layered design to convert unstructured natural language requirements into precise parameterized instructions [22][23]. Group 4 - The report emphasizes the efficiency revolution achieved through the integration of Deep Research capabilities and asynchronous parallel architecture, significantly improving response times to emerging events [67]. - The system is capable of 24/7 operation, providing scalable information services that reduce marginal costs and enhance coverage frequency [67]. - The introduction of this system aims to reshape the human-machine collaboration in research, allowing professionals to focus on deeper insights and strategic decision-making [67][68]. Group 5 - The construction process validates the feasibility of zero-code development, enabling users without technical backgrounds to quickly build and maintain investment research tools [68]. - Future enhancements suggest adopting OpenRouter interfaces for flexible model integration, allowing easy switching between different AI models [69]. - The customization of report styles and research focuses can be achieved through simple adjustments to prompt files, streamlining the iterative process of business logic [70].