Manus: How a Chinese AI Agent Is Leading the Next Revolution in Intelligent Assistants
Beyond chatbots: Inside the engineering breakthrough from China that's transforming how AI actually gets work done
This week, we dive into Manus—a groundbreaking AI agent from China that's capturing global attention for its ability to autonomously execute complex tasks. Unlike traditional AI chatbots that merely respond to queries, Manus represents a fundamental shift toward AI that can actually complete work from start to finish. Discover what sets it apart, who's behind it, and why it matters for the future of human-AI collaboration.
Introduction: A Chinese AI Agent Takes the Global Stage
Imagine telling an AI to complete a task, going for a coffee, and returning to find it has independently produced a comprehensive stock analysis report complete with interactive charts. This isn't science fiction—it's what Manus delivered in March 2025.
On March 6th, this AI Agent developed by Chinese startup Monica exploded onto the tech scene, rapidly spreading from Chinese tech circles to international platforms in just days. When Twitter (X) founder Jack Dorsey retweeted and liked Manus's demo video, and Hugging Face's product lead called it "the most impressive AI tool I've used," it became clear: a genuine AI agent revolution had begun.
"Manus is the most impressive AI tool I've tried. Its agency capabilities are mind-blowing, redefining what's possible. The user experience delivers what many other tools have promised... but this time it actually works." — Hugging Face Product Lead
For the past three years, our AI discussions have primarily focused on intelligence metrics—how smart is GPT-4? How well can Claude write code? But Manus represents a critical shift: AI must not only "think" but also "act." This paradigm change from passive answering to active execution could fundamentally transform how we interact with technology.
As someone closely watching global AI developments, I have to ask: why has a Chinese startup achieved this breakthrough in AI agency—a frontier domain supposedly dominated by American giants like OpenAI and Anthropic? Is Manus's success a fluke, or does it represent an emerging trend? Let's dive in and explore.
Manus's Positioning and Differentiation
Manus calls itself "the world's first truly general AI Agent." The name derives from Latin "mens et manus" (meaning "mind and hand"), literally translating to "hand"—symbolizing its ability to give AI models concrete capabilities to act.
The key difference between Manus and chatbots like ChatGPT or Claude lies in their working methods. When you ask ChatGPT how to complete a task, it provides guidance; when you tell Manus to do something, it completes the entire task for you. This distinction seems subtle but has profound implications.
Manus's most striking differentiating philosophy is "Less Structure, More Intelligence." This directly challenges mainstream AI application development approaches. If you're familiar with automation tools like Zapier or Make, or AI platforms like Coze, you'll notice they all emphasize guiding AI behavior through predefined workflows. Manus, however, advocates:
"Reduce human constraints on AI, let the model fulfill its potential. Don't always try to make it act human. Let it teach us how to be human."
This philosophical difference manifests technically: OpenAI's Operator and Deep Research, Anthropic's Computer Use, and similar products use graphical interfaces (GUIs), while Manus opts for a command-line terminal as its primary interaction method. This isn't merely an interface choice—it reflects a shift in thinking paradigms, from ordinary user mentality to a programmer's "Computational Thinking."
Computational Thinking involves abstracting everyday problems and solving them using systematic logical reasoning and automation tools. This approach proves particularly effective for complex, multi-step tasks—precisely where Manus excels.
The Team Behind the Innovation: Creative Force from China
What kind of team created Manus? Three key figures formed a complementary and highly creative combination:
Xiao Hong (Red): Monica's founder and CEO, a quintessential internet-era serial entrepreneur. As an internet product expert, he previously developed and sold two successful WeChat ecosystem tools. When the AI wave arrived, he swiftly seized the opportunity, demonstrating his ability to transform technology into commercial success.
Ji Yichao (Peak): Co-founder and Chief Scientist, dubbed "China's Zuckerberg" by investor Xu Xiaoping. He began entrepreneurship in high school, developing Mammoth Browser and the Magi knowledge search engine. Last October, he open-sourced a reasoning model that laid the groundwork for Manus's technical foundation. Ji has high hopes for Manus, stating: "I hope Manus is the last product I'll ever make. Because for any future creative ideas, 'Leave it to Manus'!"
Zhang Tao (hidecloud): Product Partner with 15 years of product experience across multiple top tech companies. His proposed "PPD metric" (PM to Prompt Distance—how many hands a prompt passes through before reaching the model) suggests successful AI products require product managers to directly participate in AI optimization.
Interestingly, the Manus team's pivot mirrors that of the American Arc browser team. On October 26, 2024, Arc browser founder Josh Miller announced abandoning their existing product to develop an AI Agent browser called Dia. That same day, Zhang Tao and Ji Yichao had just decided to terminate their AI browser development. This synchronicity across the Pacific indicates that top global innovators face similar technological challenges and opportunities.
Technical Deep Dive: Manus's Three Pillars
If we compare Manus to a new employee, its operation relies on three core pillars:
Pillar One: "Give it a computer"
Imagine you've just hired a remote assistant—naturally, your first step is providing them with a computer. Manus follows the same principle; instead of running in your browser, it has its own cloud computing environment.
Technically, Manus creates an isolated Docker container for each task, providing a virtual machine environment. This allows the AI to operate freely—browsing the web, writing code, processing data—without interfering with the user's normal activities.
Pillar Two: "Give it data access"
Just as you'd configure company system access for a new employee, Manus connects to numerous private APIs and data sources, enabling the AI to retrieve authoritative, structured information.
Manus employs a multi-agent architecture using the "ReAct" mode (a method combining Reasoning and Acting). The system first uses a planning tool to break down tasks, then dynamically assigns specialized agents based on task type—search agents, coding agents, data analysis agents, etc.—each equipped with specific toolsets.
Pillar Three: "Give it some training"
New employees need to understand company culture and working methods; AI similarly needs to adapt to user preferences. Manus learns from user feedback, continuously adjusting its working style, developing an evolutionary capability.
Notably, the Manus team created an entirely new metric: AHPU (Agentic Hours Per User). They believe future AI product success shouldn't be measured by daily active users (DAU) but by how long users employ the AI agent. This reflects a paradigm shift from "competing for attention" to "replacing actual work hours"—a disruption of Silicon Valley's traditional product thinking.
For models, Manus combines Claude, Qwen, and others. A single task reportedly costs about $2, lower than industry averages but still significantly higher than traditional AI chat services—explaining why Manus limits users through invitation codes.
Real-World Experience and Limitations
As Manus gains visibility in international tech circles, authentic user experiences are beginning to emerge on social media platforms, offering glimpses into what this technology can actually accomplish.
AK (@_akhaliq), a well-followed AI researcher and developer, shared his experience using Manus to create a three.js endless runner game. With a simple prompt, Manus autonomously generated a fully functional 3D game with physics, scoring, and visual elements—a task that would typically require significant development expertise. His demonstration video quickly garnered over 230,000 views, showcasing Manus's ability to generate complex interactive applications from simple instructions.
On the investment side, Deedy Das, a venture capitalist at MenloVentures with a strong technical background from Google, used Manus to analyze Tesla stock. He reported that the system accomplished in approximately one hour what would typically require a financial analyst about two weeks of work. This efficiency demonstration caught the attention of several Wall Street technology advisors.
However, like any emerging technology, Manus exhibits significant limitations:
Performance speed: Protocol's David Pierce, known for his rigorous tech product testing, documented that complex tasks regularly required 30-60 minutes to complete—potentially limiting real-time use cases.
Reliability issues: MIT Technology Review's Will Knight noted inconsistent results across similar tasks, with approximately 20% of complex requests experiencing execution failures or infinite loops.
Practical function gaps: TechCrunch's comprehensive testing revealed that Manus struggled with seemingly simple tasks. Their reporter attempted to use it for ordering food and booking flights—both ending in failure, despite these being routine activities for human assistants.
Context management: Several enterprise technology evaluators identified limitations in Manus's ability to maintain context during extended operations, particularly during programming tasks or multi-stage research projects.
Despite these constraints, the growing number of successful use cases shared on social media suggests Manus represents a significant advance in AI agent technology. While it's clearly not perfect, it demonstrates a viable path toward autonomous AI agents that perform actual work rather than merely generating content—a development that international observers are watching with increasing interest.
Open Innovation: The Wave of Open-Source Replications
Manus's emergence quickly ignited creative enthusiasm in the open-source community. Within just one day of its release, multiple open-source replication projects appeared—an unusual phenomenon in the AI field.
OpenManus was completed by several young developers in just three hours. Unlike Manus, it allows AI to directly control the user's computer, including web browsing and code execution. Despite limited functionality, the project rapidly garnered over 26,900 stars on GitHub, demonstrating the developer community's immense interest.
The OWL (Optimized Workforce Learning) project is more ambitious. It achieved first place among open-source projects in the GAIA benchmark test (an authoritative test set evaluating AI assistants' general capabilities) with a score of 58.18. The GAIA test includes 466 carefully designed questions spanning simple information queries to complex multi-step problem solving—the gold standard for assessing AI Agent capabilities.
The rapid emergence of these open-source projects sparked an interesting discussion: Does Manus truly have technological barriers? The answer seems to be: while the core technical architecture can be replicated, high-quality engineering implementation and user experience remain differentiating advantages. This resembles the relationship between the iPhone and early Android phones—concepts can be imitated, but details and experience are difficult to copy.
For Western developers and investors, this open-source replication wave suggests that AI agent technology has reached a stage of rapid iteration and large-scale application, no longer limited to major tech companies' laboratories. This portends more innovative products emerging in the next 12-24 months that could fundamentally change how we use AI.
Broader Reflections: The Future Landscape of AI Agents
Manus's emergence isn't merely a product event but marks a turning point in the AI industry. Let's take a broader perspective and consider several key trends:
Explosive Growth in Computing Power Demand
According to the Manus team, AI Agents consume tokens at an "exponential increase" compared to traditional chatbots, with 2025 inference demands projected to grow 100-1000 fold. This presents enormous opportunities for NVIDIA and cloud service providers but also means AI service cost structures will undergo fundamental changes.
Comparing OpenAI's GPT-4o and DeepResearch, though they may be more advanced in model architecture, Manus achieves similar or even better user experiences through meticulous engineering implementation. This suggests model intelligence is only part of the equation—engineering quality and product design are equally crucial.
Transformation of Human-Machine Collaboration Models
Manus represents a new human-machine collaboration model: shifting from humans controlling AI through interfaces to humans stating requirements and AI independently completing tasks. This "delegate-deliver" model will redefine knowledge work, potentially automating many intermediate processes.
This change will profoundly impact Western companies and employees' working methods. Imagine when every knowledge worker has an AI agent with capabilities equivalent to a junior assistant—how will productivity improve? This transcends efficiency questions, concerning fundamental transformations in work nature.
Differences in US-China AI Innovation Paths
It's worth considering why this breakthrough came from China rather than Silicon Valley. I've noticed an interesting pattern: American companies like OpenAI and Anthropic focus more on breakthrough foundational model development, while Chinese enterprises tend to combine existing technologies into innovative products.
Manus team members once shared: "The model's intelligence has evolved to the stage where it can function as an Agent!" This indicates they keenly captured the convergence point between technological maturity and market demand, rather than pursuing purely theoretical breakthroughs.
This difference might explain why Chinese companies may possess certain advantages in AI application—rapid iteration, practical engineering implementation, and attention to user experience perfectly align with success factors in the AI agent domain.
Conclusion: New Beginning or Marketing Bubble?
So, is Manus a genuine breakthrough in the AI agent field, or simply a successful marketing campaign? I believe the answer lies somewhere in between.
From a technical perspective, Manus's innovation resides more in clever engineering implementation and product design than revolutionary breakthroughs in underlying algorithms. As one commentator noted: "Manus, this so-called super amalgamation, has drilled a huge hole in the thick wall between AI's capability and usability, revealing the vast expanse of applications beyond."
For those of us observing AI development, Manus offers several important insights:
The next frontier in AI applications is autonomous execution capability, not merely increasing model intelligence.
Excellent engineering implementation and product design are key success factors for AI products.
Chinese enterprises' innovation speed in AI applications cannot be ignored, with more globally influential products likely to emerge.
The positive interaction between open-source communities and commercial products will accelerate AI agent technology development.
We're in the early stages of AI agent technology development, and Manus is just the beginning. As underlying model capabilities continuously improve and engineering practices accumulate, future AI agents will become more powerful, flexible, and practical.
I'd love to hear your thoughts: Do you think Manus represents the future direction of AI? How would you use this technology to change your work methods? Please share your ideas in the comments section, and let's explore the unlimited possibilities of AI agents together!