The endgame of the Agent track is not who is the smartest, but who enables the most people to own an Agent.

Byline: Deep Think Circle

Have you noticed a strange thing? Every time you ask an AI to help you with the same task, you have to teach it again. Today it organizes data, and tomorrow the same kind of task needs to be explained from scratch again. If AI is getting smarter and smarter, why are we still doing repetitive work?

On March 30, 2026, a Silicon Valley AI company called CREAO released a product that offered a different answer. The moment the product launched, it dominated the global Hot Search Top 3 on the X platform for five straight hours, sparking a wave of spontaneous discussions from tech creators and developers across North America, Europe, Southeast Asia, Latin America, and other regions. After digging into the product, I found that what they’re doing is completely different from all AI Agent products on the market. This China-U.S. blended team from top Silicon Valley players like Google and Meta has found a path that everyone else has overlooked.

The Real Dilemma of Current AI Agents

First, I need to state a fact: the AI Agent space really did become hot in 2025 to 2026. OpenClaw, Claude Code, Devin, and also DeepSeek domestically—these products have helped many people use AI Agents for the first time in a genuinely practical way. But once people start using them, new problems emerge, and this problem is much more serious than you’d expect.

I’ve encountered this kind of scenario myself. Last week, I asked Claude Code to write me a data scraping script. It took about twenty minutes of back-and-forth conversation—adjusting details—until it finally ran successfully. This week, I wanted to scrape another website’s data using the same logic. In theory, I only needed to change a few parameters, but I discovered I had to open a brand-new chat window, explain my needs again from scratch, and adjust the details again. The AI doesn’t remember how we coordinated last time, so it can only start over. This experience made me realize that the core problem facing today’s AI Agents isn’t a lack of capability—it’s that every use is disposable. Use it once, and it’s gone.

What bothers me even more is that these powerful AI Agents often keep “looking for work to do.” I just wanted it to scrape price data from three websites and record it in a spreadsheet, but it started analyzing price trends, generating visualization charts, and even proactively offered to write a competitor analysis report. These features sound impressive, but I don’t need them at all. The AI is demonstrating the boundaries of its abilities instead of focusing on solving my specific problem. This kind of generalization is thrilling in demos, but in real use it creates a massive cognitive burden—I have to spend time stopping it from doing things I don’t need, and repeatedly stressing that I only want the simplest possible data scraping.

Cost-effectiveness is also a major issue. When you ask a general-purpose AI Agent to execute a simple repetitive task, each time it has to reinterpret your intent, re-plan an execution path, and re-invoke various tools. This process is not only time-consuming; if you’re using a token-based billed API, the costs add up quickly. I did the math: if I use Claude or GPT-4 to run a simple data synchronization task that’s scheduled every day, the monthly API call costs might end up being higher than hiring an intern to do the work manually. That’s completely unreasonable.

I’ve discussed this with some developer friends, and everyone’s impression is very consistent: AI Agent capabilities are evolving quickly, but usability is degrading to some extent. In the past, with automation tools like Zapier or n8n, configuration was troublesome, but once set up, they ran reliably without requiring repeated investment. Now that we have AI Agents, configuration is simpler—but every time you still have to reconfigure. This isn’t progress; it’s replacing old complexity with a new kind of complexity. The core contradiction is not that ordinary people can’t use AI Agents, but that they can’t use them stably, can’t keep them, and can’t turn a successful conversation into a reusable automation system.

CREAO’s “Taming” Philosophy

When I first saw CREAO’s product demo, my first reaction was: this is exactly what I’ve been looking for. They gave this product a very interesting positioning: Agent Harness. In Chinese, you can understand it as “Agent taming.” The term is very accurate in describing what they’re doing—not making AI stronger, but making AI’s capabilities something that can be solidified, tamed, and controlled by ordinary people.

CREAO’s core experience is very direct. You describe a workflow in natural language—such as, “Every Monday at 9 AM, scan the price changes on three competitor websites, record them in Google Sheets, and if the fluctuation exceeds 10%, notify me on Slack.” The system will do things like: understand your intent, automatically write the execution code, and connect the tools you need (Gmail, Google Sheets, Slack, Feishu, etc.—they’ve already integrated more than 300 platforms). Then, most importantly—this entire workflow can be saved instantly as an Agent. You set a schedule to run it periodically, and afterward it will execute automatically according to the times you set, without requiring AI involvement. It’s completely deterministic execution.

This last step is the soul of the entire product. After the conversation ends, the system keeps running. This sounds simple, but it solves the problem the whole industry has been avoiding. With ChatGPT, Claude, and other conversational AI products, once you close the window, everything is gone. With OpenClaw and Claude Code developer tools, they can execute complex tasks, but you still have to deploy and maintain them yourself. What CREAO does is combine the flexibility of AI with the determinism of traditional automation tools, so that a single successful AI conversation can be transformed into a durable, continuously running automation system.

I especially appreciate the trade-offs they made technically. Many AI Agent products aim to make AI smarter, more general-purpose, and able to handle more complex tasks. CREAO chose the opposite path: they want the workflows generated by AI to be able to run independently of AI. That means they need to solve the determinism problem of code generation—AI-generated code must be stable enough to keep running without AI intervention. They also need to solve the stability problem of multi-tool orchestration—when a workflow involves multiple platforms like Gmail, Sheets, and Slack, how do you ensure data transfer between them won’t fail due to format issues. These are engineering problems that traditional automation tools have already solved over the past decades, but in the context of AI Agents, they need to be solved again—because the workflow is no longer manually configured by people; it’s generated by AI from natural language.

I tried it myself, and the real experience is indeed different from other products. I described a requirement in natural language: “Every day at 5 PM, automatically summarize the emails in my Gmail inbox marked as important, extract the sender and subject, write them into a Google Sheets spreadsheet. If there are client emails, @ me in the Feishu group.” The entire configuration took less than 5 minutes. During setup, I could see in real time what CREAO was doing—generating code, testing connections, and validating logic. Once the configuration was done, I clicked a “Save as Agent” button, set it to run every day at 5 PM, and then didn’t have to manage anything. The next day at 5 PM, I really received a notification in the Feishu group, and when I opened Google Sheets, the data had already been organized exactly according to my request. The key to this experience is that I don’t have to open CREAO’s chat window at 4:55 PM every day to describe my needs again. It’s like a tamed assistant that knows what it should do each day and can just do it.

Native integrations for more than 300 platforms are also an important product advantage. That means for most common workflow scenarios, CREAO already has ready-made connectors. Users don’t need to hunt down API documentation, configure authentication, or handle low-level details like data format conversions. If you say “write data to Google Sheets,” the system knows how to do it. If you say “send a message on Slack,” it also knows how to do it. The smoothness of this experience is hard to match by writing code yourself or using traditional automation tools. I believe this is exactly how CREAO’s team understands consumer-grade products—lower the configuration cost so ordinary people can quickly build their own automation systems.

Not the strongest—just the easiest to tame

While researching CREAO, I kept wondering: why didn’t other companies doing AI Agents choose this route? Later I realized it’s because there are two completely different product philosophies competing with each other.

Look at Anthropic’s Claude Code, or Cognition’s Devin. Their goals are to build the most powerful general-purpose Agents. These products hope AI can understand any request, execute any task, and even make decisions autonomously without explicit instructions. That’s a path of “making Agents smarter.” In this path, product value comes from the AI’s generalization ability—it can handle more complex problems, make correct decisions under more uncertainty, and get closer to how human developer teams work. That direction is obviously valuable, but it’s inherently geared toward developers and professional users, because only they need and can manage this level of flexibility.

CREAO chose a different path: instead of building the strongest Agent, they build the Agent that’s easiest for ordinary people to tame. Their product value isn’t about how smart the AI is, but about how easily ordinary users can solidify the AI’s capabilities into their own personal tools. In CREAO’s product philosophy, a good Agent isn’t one that can do everything—it’s one that can reliably do one thing well, and that can be reused. This kind of convergence—precisely what consumer-grade products need most.

I thought of a good analogy. A general-purpose AI Agent is like an all-purpose consultant—you can go to it whenever you have a question. It can give you lots of advice, but you still have to explain the background every time, describe your needs, and discuss the plan. What CREAO creates is a trainable assistant. You teach it once how to do something, and then it will periodically go do it on its own without you repeatedly guiding it. The former shows breadth of capability; the latter provides efficiency of use. For ordinary users, efficiency matters far more than capability.

The difference in this product philosophy has already been validated by market response. On the day CREAO was released, more than 50 top tech KOLs worldwide simultaneously published deep experience content, covering markets across multiple languages such as English, Spanish, Portuguese, and Korean. This kind of multilingual spontaneous spread is extremely rare. It shows that the problem CREAO solves is global and cross-cultural. No matter whether you’re in North America, Europe, Southeast Asia, or Latin America—if you’re an ordinary user who needs to handle repetitive workflows, this product will attract you. The market has already voted with its feet. People don’t need more powerful AI—they need AI that’s easier to control.

I also noticed an interesting comparison. If you look at products that pursue general Agents, their demo cases are usually like: “AI helped you complete a complex development task,” or “AI independently analyzed a business problem and provided a solution.” Those cases are very impressive, but they’re hard to replicate. After ordinary users watch them, they think, “Wow, that’s amazing,” but they don’t know how to apply it to their own work. CREAO’s use cases are all very concrete: monitoring competitor prices, syncing data to spreadsheets, sending reports on a schedule, organizing emails, managing to-do items. These are things everyone does every day—only now they can be automated. This difference in product positioning gives CREAO a naturally broader user base.

Between conversational AI and traditional automation systems, CREAO has found a clever balance. It preserves the ease of conversational AI—using natural language to express needs, without having to learn programming or research complex configuration interfaces. It also inherits the reliability of automation systems—once configured, it can execute deterministically and won’t produce unexpected outcomes due to AI randomness. This balance is extremely rare, because most products swing between these two extremes: either too flexible and unstable, or too fixed and not smart enough. CREAO enables users to enjoy AI flexibility during the configuration phase, and to enjoy automation determinism during the execution phase.

Product Insights from a Silicon Valley Team

I’m curious what kind of team could build a product like this. After going deeper, I found that CREAO’s headquarters is in Silicon Valley, and its core team brings together Chinese AI elites from top Silicon Valley companies like Google and Meta, as well as technical backbones from leading domestic large-model startups and star internet companies. This is truly a China-U.S. blended team in the real sense.

I think the team background matters a lot. Engineers from Silicon Valley big tech have a very deep understanding of underlying technologies—they know how to build stable and reliable systems. Product managers and engineers from domestic internet and AI companies, on the other hand, have a strong sensitivity to C-end user experience. They know what kind of product design can genuinely lower the usage threshold. The combination of these two kinds of “genes” has produced a project like CREAO—one with both deep technical strength and product “warmth.”

As far as I understand, the CREAO team spent months specifically solving a problem: how to make AI output remain “alive” even after the conversation ends. This problem may seem simple, but behind it there are many technical challenges. Code generated by AI is naturally random—two different generations from the same requirement description could be completely different. How do you ensure the generated code is stable enough to keep running continuously without human intervention? How do you handle exceptional cases—if an API call fails, should the system retry, degrade gracefully, or notify the user? How do you ensure data passing between multiple tools won’t break due to format issues? These are engineering problems that traditional automation tools have already solved over the past decades, but in an AI Agent scenario, these issues need to be rethought and solved again, because the way workflows are generated has changed.

What impresses me especially is that the CREAO team didn’t choose a simple solution. They could have saved the generated workflows like many AI products do, letting users manually trigger execution every time. That would reduce technical difficulty a lot, but it would severely degrade user experience. CREAO chose real automation—scheduled runs, autonomous execution, exception handling, and logging. Those are standard features of traditional automation systems, and CREAO has them too, implemented based on AI-generated workflows. This requires finding a precise balance point between AI flexibility and system stability, as well as a lot of engineering accumulation and product refinement.

Another point that really stands out to me is that CREAO’s underlying architecture, execution engine, and integration protocols are all developed in-house. In today’s AI startup environment, many companies choose a fast “shell-on” approach—based on OpenAI or Anthropic APIs, add a frontend interface, and you can ship a product. That can validate the market quickly, but it’s hard to build real technical barriers. CREAO’s team chose the harder path: they built from the bottom up, ensuring that every part of the system is under their own control. This technical investment may not show clear advantages in the short term, but in the long run, it’s the only way to build competitive moats.

It’s also worth mentioning that within one year, CREAO has already completed three rounds of fundraising totaling tens of millions of dollars, and after the product launch, it has drawn widespread attention from the capital markets. This indicates investors also see value in this direction: in the AI Agent space, it’s not about whose model is biggest or whose Agent is the smartest that wins. It’s about who can truly convert AI capability into products that ordinary people can use—and whoever does that will take the high ground in the market.

The True Endgame of the Agent Track

After researching CREAO, I’ve had some new thoughts about the AI Agent space. I think the endgame of the Agent track isn’t about whose Agent is the smartest—it’s about who enables the most people to have their own Agents. This is a fundamental shift in how we recognize things.

Over the past two years, the whole industry has been competing on model capability, competing on Agent frameworks, competing on developer tools. Everyone is comparing who can make AI complete more complex tasks, and who can achieve higher autonomy with less human intervention. This competitive logic has strong appeal within the tech community because it matches engineers’ aesthetics—pushing to limits, challenging boundaries, and breaking the impossible. But from a business and product perspective, this might not be the most important battleground. The real battleground is: how to lower the usage threshold, how to increase reusability, and how to let ordinary people also enjoy the efficiency gains brought by AI Agents.

The path represented by CREAO, in essence, pursues “lowering the taming barrier” rather than “improving general capability.” These two directions aren’t mutually exclusive, but they serve different markets. For developers and professional users, they indeed need stronger general-purpose Agents, because their needs are themselves complex and changeable. But for ordinary people who make up more than 90% of the user base, they need dedicated Agents that can reliably solve specific problems—not an all-purpose assistant that can do everything but has to be taught again and again each time. CREAO is targeting that 90% market.

I especially agree with one viewpoint: reusability is the next battleground for consumer-grade AI. Most AI products on the market today—whether ChatGPT, Claude, or various Agent tools—are basically one-time consumption. The user asks a question, the AI gives an answer, and the value of that conversation ends there. Even if the AI provides a great solution, when users face a similar problem next time, they still have to ask again, wait again, and verify again. In this model, AI’s value grows linearly. If you use it 10 times or 100 times, the total value is just simple addition. But if AI’s outputs can be reused—such as continuously running after a single configuration—then the value grows exponentially. Configure once, use it a hundred times, and you don’t have to reinvest effort every time. What CREAO does is turn one-time consumption into reusable assets.

This reminds me of a classic transition in the software industry. In the early days of software development, every feature required writing code from scratch. Later, with function libraries, frameworks, and components, developers could reuse code written by others, dramatically improving efficiency. Then came low-code and no-code platforms, enabling even people who can’t program to build applications. The evolution path of AI Agents might be similar: at first, every conversation has to start from zero; then come Agents that can be saved and reused; and finally, there may be an Agent marketplace where people can share and exchange the Agents they’ve tamed. What CREAO is doing now is precisely a key leap from the first stage to the second stage.

My judgment is that AI Agents will split into multiple different product forms, each serving different user groups and usage scenarios. There will be Agents that pursue extreme generality, serving developers and professional users. There will also be Agents focused on specific vertical domains—such as law, healthcare, and finance. And there will be Agent platforms like CREAO that focus on consumer-grade automation. These directions are not competitive relationships; they are symbiotic. Together, they form a complete AI Agent ecosystem. In this ecosystem, the consumer-grade track that CREAO chooses is likely the one with the largest user base and the widest commercial potential.

From “the strongest Agent” to “the Agent for the most people,” this is not only a change in positioning—it’s a redefinition of AI value. AI’s value shouldn’t only be measured by how difficult of a task it can accomplish; it should also be measured by how many people it helps improve efficiency, solve problems, and improve daily life. Products like CREAO show me the possibility that AI is truly moving toward the mass market. When everyone can have their own dedicated Agent, and when those repetitive, mundane, time-consuming tasks in daily work can be handled automatically, then AI can truly fulfill its mission—not replacing human beings, but freeing them from mechanical labor so they can do more creative and more valuable things.