前言 过去几个月里，我们偷偷做了一件"体力活 + 心力活"都拉满的事情。 我们把 Reportify 从底层拆开，又重新焊了回去。 Reportify 3.0 经历了长达数十天的内测后，终于在前几天正式和大家见面了。内测这段时间里，我们收到了大量来自用户朋友们的反馈，有拍桌子的，也 有夸得飞起的。每条建议我们都看了，也都改了。 你想象一下：每天 10 点，特斯拉投研助理自动给你发日报；早上起床，中国宏观和美股宏观两个助理已经把今天的重点都整理好了。 会不会像我一样，露出老父亲般欣慰的笑容 这就是我们希望 Reportify 3.0 给你带来的体验：不是一个工具，而是一群 AI 同事。 闲话少叙 解锁 Reportify 3.0 让AI成为你的专属投研助理 投资的关键 不仅是把逻辑弄清楚，而是持续跟踪关键变量 这一次，我们不是在做一个"版本更新"。 说句可能有点"技术味"的话：Reportify 3.0 是一次底层范式的重启。 从 1.0 的 Copilot 模式，到 2.0 的 Deep Search 框架，到今天的 Agent 平台化，我们跨过了那个过去只能憧憬、现在终于能落地的时代：AI 不再只是帮你总 ...

Core Viewpoint - The article discusses the advancements in Multi-Agent Systems (MAS) and introduces "Agentic Workflow" as a key concept for autonomous decision-making and collaboration among intelligent agents, highlighting the emergence of structured and verifiable workflow frameworks like "MermaidFlow" [1][4][22]. Group 1: Introduction to Multi-Agent Systems - The development of large language models is driving the evolution of AI agents from single capabilities to complex system collaborations, making MAS a focal point in both academia and industry [1]. - Leading teams, including Google and Shanghai AI Lab, are launching innovative Agentic Workflow projects to enhance the autonomy and intelligence of agent systems [2]. Group 2: Challenges in Current Systems - Existing systems face significant challenges such as lack of rationality assurance, insufficient verifiability, and difficulty in intuitive expression, which hinder the reliable implementation and large-scale deployment of MAS [3]. Group 3: Introduction of MermaidFlow - The "MermaidFlow" framework, developed by researchers from Singapore's A*STAR and Nanyang Technological University, aims to advance agent systems towards structured evolution and safe verifiability [4]. - Traditional workflow expressions often rely on imperative code like Python scripts or JSON trees, leading to three core bottlenecks: opaque structure, verification difficulties, and debugging challenges [7][10]. Group 4: Advantages of MermaidFlow - MermaidFlow introduces a structured graphical language that models agent behavior planning as a clear and verifiable flowchart, enhancing the interpretability and reliability of workflows [8][12]. - The structured representation allows for clear visibility of agent definitions, dependencies, and data flows, facilitating easier debugging and optimization [11][14]. Group 5: Performance and Evolution - MermaidFlow demonstrates a high success rate of over 90% in generating executable and structurally sound workflows, significantly improving the controllability and robustness of agent systems compared to traditional methods [18]. - The framework supports safe evolutionary optimization through a structured approach, allowing for modular adjustments and ensuring compliance with semantic constraints [16][19]. Group 6: Conclusion - As MAS and large model AI continue to evolve, achieving structured, verifiable, and efficient workflows is crucial for agent research, with MermaidFlow providing a foundational support for effective collaboration processes [22].