Microsoft China CTO + 3 Claw Product Builders: From Lobster Working to Lobster Business | Ronghui
Practical Claw Applications Across Three Scenarios: Personal Agents, Financial Research, and Cloud Assistants
Since OpenClaw 🦞 ignited the Agent ecosystem, industry discussion and practice have gradually moved into deeper territory. What is the essence behind OpenClaw's explosive popularity, and how can we push Agent from technology toward real enterprise deployment? What foundational capabilities do continuously evolving SOTA models provide, enabling Agents to execute complex tasks faster and more reliably? When deploying "little lobsters" and Agents at the enterprise level, how can we balance efficiency and security while truly creating business value?
Recently, Gaorong Ventures' Ronghui organized the second installment of its "Agent New Paradigm" event series. Qing Wei, CTO of Microsoft China, along with senior Microsoft technical experts and three builders developing Claw applications across personal Agent, financial investment research, and cloud assistant scenarios, shared their understanding of OpenClaw's essence and best practices for enterprise-grade Agent deployment.
I. Future Organization Insights: Machines Provide "Regression Values," Organizations and People Provide "Outlier Values"
In today's AI era, we have entered a high-speed evolution cycle where "talent emerges with each generation, each leading the trend for mere weeks." Qing Wei, CTO of Microsoft China, quoted Han Yu's principle of "grasp the spirit, not the words" as his framing approach — making fewer absolute assertions and more hands-on attempts is the correct posture for embracing AI transformation.
He emphasized that "the current AI transformation is no longer simply digital transformation, but a reformation, even a renaissance of technology and thought, ultimately an enlightenment driving civilizational reconstruction." In this transformation, the real challenge has never been technology, computing power, or resources, but rather the cognitive and mastery capabilities of organizations and people — "AI has arrived; it simply cannot yet be harnessed."
Wei further explained that what machines excel at providing are "regression values"; the value of organizations and people lies in providing "outlier values" — scarce subjective initiative, taste, and values. These "outlier values" stem from human foresight regarding environment and culture.
"In future organizations, those who can offer differentiated thinking based on cultural trajectories and shifts in human sentiment will be the most scarce and precious." This means the AI era is not about machines replacing humans; right now is precisely when every individual's subjective initiative is most needed.
Drawing on extensive enterprise case studies, Wei stressed that the prerequisite for enterprise-grade Agent deployment is organizational process reengineering, data governance, and behavioral modeling. Only after achieving standardization across APIs, data, definitions, and processes — reaching the foundation of "unified script, unified track width" — can Agents break down departmental silos and enable horizontal collaboration. On this basis, people provide innovation and "outlier values," allowing business to achieve efficient governance.
Wei also shared the three stages Microsoft R&D teams experienced when developing new AI products. The first stage was "AI for XXX" — adding AI capabilities on top of existing products. The second was "XXX for AI" — reconstructing products around AI. Only when AI becomes "technology dissolved into the invisible," permeating every aspect of the product, is true evolution complete.
II. SOTA Models × OpenClaw: Enabling Enterprise-Grade Agent Deployment
Senior Microsoft technical experts Yan Chen and Bin Liu then shared from a technical practice perspective how to leverage the Microsoft ecosystem, SOTA models, and OpenClaw to move Agents from task execution to revenue generation.
Chen noted that Microsoft's Microsoft Foundry platform, launched in 2025, has integrated over 11,000 cutting-edge models, including first-day access to OpenAI's latest GPT-5.4, as well as top models from Anthropic, Meta, and others — providing rich underlying support for Agent development. GPT-5.4 in particular is a model purpose-built for Agentic-native applications.
"For Agents to automatically execute complex tasks, they must cross the chasm from single-step reasoning to long-chain execution stability." Chen pointed out that large-scale Agents previously had five major failure modes: state continuity interruption, trajectory drift, tool invocation drift, contract non-compliance, and long-chain error accumulation.
GPT-5.4 addresses these issues with targeted technical optimizations, including introducing continuous state runtime, reducing volatility in multi-step execution trajectories, strengthening contract adherence, improving system-level reproducibility, and introducing safety stability — thereby achieving the leap from "can do things" to "can run long-term, run stably, with verifiable results." "GPT-5.4 is like a barrel with no obvious weak planks; its overall performance is very stable, so you can confidently hand a task over to it." This is the prerequisite for Agents to truly achieve enterprise-grade application.
Liu emphasized in his sharing that for Agents to deploy at scale in business and generate real revenue, they must thoroughly clarify three core dimensions: understanding employees' real working methods, understanding business operational logic, and doing organizational knowledge management well.
At the employee level, this means understanding real working methods and integrating documents, meetings, chats, and business data as decision-making foundations, establishing long-term working memory for individuals and teams, thereby converting context into real-time insights and optimal decisions. At the business level, this requires unified business semantics as a foundation, maintaining 24/7 availability and timely response, with complete business context preserved. At the organizational knowledge management level, this means automated data understanding and full-scenario context coverage, while strictly adhering to access permission rules.
In summary, only by fully connecting employees, processes, and knowledge — building sufficient contextual support — can Agents truly bridge the gap from technology to business.
III. Three Scenario Claw Application Practices: Personal Agent, Financial Investment Research, Cloud Assistant
During the sharing, three Gaorong portfolio companies also discussed how they are exploring OpenClaw and Agent applications in vertical scenarios.
Personal Agent: MindClaw
Andrew Chen, co-founder of Mindverse, noted in his sharing that the essence of OpenClaw is packaging AI Coding and Agent capabilities into a chat-based interface for ordinary users who can't code, delivering an unprecedented experience.
Today the entire industry is actively embracing the OpenClaw ecosystem and building products within it. "To some extent, OpenClaw has built the Android ecosystem of the AI era. How you make the MacBook of this era, and the Huawei, Xiaomi, and Samsung phones — that's the opportunity for each of us," Andrew said. "This also makes us as entrepreneurs very excited. We're all fellow travelers, all exploring different futures, and so far the real competition is very thin."
Mindverse is dedicated to using LoRA to let everyone have their own large model, achieving thousand-person-thousand-face Personal Intelligence. The company has built a trillion-parameter LoRA reinforcement learning system. Globally, only Mindverse and Thinking Machines Lab — founded by OpenAI's former CTO — have achieved trillion-parameter LoRA reinforcement learning. Additionally, Mindverse focuses on developing consumer-grade apps for overseas multilingual users, covering over 4 million users, including the Personal Agent product Macaron AI.
Recently, Mindverse launched MindClaw, a personalized long-term memory solution based on OpenClaw.
Regarding key bottlenecks in OpenClaw application deployment, Andrew identified memory, testing, and safety as three directions awaiting breakthrough, with long-term memory capability being core to constraining Agents' execution of complex tasks. MindClaw proposes a solution with parametric memory coexisting with a File System — essentially training a dedicated LoRA model for each user's OpenClaw that continuously updates through conversation, "gradually compressing user preferences into model parameters," enabling the model to continuously evolve and become increasingly personalized.
Financial Investment Research Agent: AlphaClaw
In early March, AlphaEngine from Shangjian Technology launched AlphaClaw, positioned to help financial users raise a lobster that can do investment research — that is, to build users their own private investment research workbench.
Shangjian Technology aims to become an AI infrastructure provider for the financial asset management sector. Its AI investment research engine AlphaEngine for institutional investors currently covers over 8,600 asset management institutions and 80,000 institutional investors.
Fei Binjie, founder and CEO of Shangjian Technology, explained that compared to OpenClaw, AlphaClaw has several core advantages: one-click installation with zero learning curve; local-first architecture ensuring data security; and most importantly, backing from AlphaEngine's massive financial database, including research reports from domestic and foreign securities firms, meeting minutes, commentary, and other voluminous content.
Based on AlphaClaw, users have unlimited play possibilities. For example, generating a skill based on Warren Buffett's stock selection logic, letting a "Warren Buffett avatar" pick stocks for you; completing quantitative factor strategy backtesting with zero code, visually, adding quantitative wings for active investors; helping primary market investors read relevant materials and directly write investment committee reports with PPT generation; or having AlphaClaw first read extensive quality Chan Theory reports, learn the Chan Theory technical research framework, then automatically write code and perform Chan Theory analysis on stocks — going further to generate daily stock recommendations based on Chan Theory skills.
Fei emphasized that AlphaClaw's original intention is "let research return to thinking," becoming a super efficiency multiplier for financial professionals. Transactional work will soon be easily achievable through AlphaClaw, allowing financial professionals to devote more time to forward-looking industry research, company business exchanges, and other core work.
Experience at: www.alphaengine.top
Cloud Exclusive Assistant Agent: ToClaw
As a remote desktop tool, ToDesk enables users to extend their hands and eyes to remote devices, having accumulated broad user adoption. ToClaw is ToDesk's OpenClaw-based secondary development for a personal cloud exclusive assistant — "essentially providing an AI brain, combined with remote desktop tools, to significantly enhance individual or enterprise users' device usage efficiency."
Zhu Min, co-founder and CTO of ToDesk, shared that ToClaw requires no manual deployment, while incorporating ToDesk's remote connection capabilities and atomic operation capabilities, enabling multi-device execution and cloud memory. For users, ToClaw completely breaks device silos — every device logged into the same account becomes an avatar. Users can sit on their couch using a tablet to instruct Device A to find files, while having Device B run web crawlers in the background, and directing Device C to render remotely. Users are no longer operating one computer, but commanding a "device cluster."
Additionally, ToClaw provides supporting security management and permission control capabilities. While ToClaw is working, users can intervene in real-time in remote device operations, ensuring Agent actions can be monitored and interrupted.
From ToDesk's perspective, complex tasks in the future will still require human-machine collaboration. The ideal outcome is building a productivity Agent that is both secure and efficient, based on the OpenClaw architecture, model capabilities, and ToDesk's security system. "In the future perhaps everyone will have dozens of avatars, but we hope all avatars are trustworthy, reliable, and secure."
As SOTA models continue evolving, platform ecosystems keep improving, and industry pioneers continue exploring and optimizing the OpenClaw architecture, we believe many more interesting application possibilities lie ahead. We also look forward to seeing Agents more widely enter real business scenarios, creating perceivable value.
