HyT Capital Continues to Back | Creao AI Secures Another Round of Multi-Million USD Financing to Build a Closed-Loop Agent OS System
The following article is sourced from the "Z Potentials" WeChat public account.
This company has just completed a multi-million USD financing round, led by Prosperity7 Ventures — the diversified venture capital fund under Aramco Ventures — and MPC (formerly Matrix Partners China), with follow-on investments from existing backers including HyT Capital, Yunqi Partners, MONOLITH, GL Ventures (Gaoling Venture Capital Co., Ltd.), and HongShan. In less than a year, CREAO AI has accumulated over USD 30 million in total financing.
The core thesis they are betting on is this: the real bottleneck in the AI industry is not intelligence itself, but the gap between "a chatbot answering a question" and "an agent doing work for you while you sleep."
Two Traps
"Everyone knows AI will bring a productivity explosion," said CEO Kai (Cheng Kai). Before founding CREAO AI, he spent ten years building production-grade AI systems for over 250 enterprise clients. "But the entire industry is stuck in two traps — if people still have to operate AI tools step by step, productivity has a ceiling; if people remain the only ones building the tools, the real AI revolution hasn't even started."
CREAO AI's solution is a closed-loop system: AI both builds the tools and runs the tools.
This closed loop is realized through a complete product architecture. CREAO's starting point is a conversation. A user describes a task to a "super agent," which does not just answer questions but executes directly — writing code, calling APIs, connecting third-party services, and delivering results in a Sandbox Environment.
The key is the next step. A successfully executed task is saved as a reusable Agentic App — a persistent, automated execution unit with independent memory and scheduled triggers. That SEO pipeline you ran on Tuesday can run itself again on Friday. No need for you to be there.
The product architecture consists of three layers:
Programming Agent — AI builds the tools. Users create Agentic Apps through conversation, with no need to write traditional code. The team calls this breaking the "builder bottleneck."
Autonomous Execution — AI uses tools. Agentic Apps run on schedule and trigger workflows automatically, without human follow-up. This breaks the "operator bottleneck."
Workspace — Humans take command. A persistent environment where Agentic Apps run, with memory accumulating across tasks. One person can manage a workload that previously required an entire team.
Together, these three layers form CREAO's Agent OS — an agent-centric operating system entry point that covers all work scenarios. In the future, whether it is content production, customer operations, data analysis, or code delivery, everything will start here, be deposited here, and be driven from here.
"If AI builds the tools, but people still have to click 'run' every time, then we haven't won," said Cheng Kai. "The core is closed-loop: AI builds, AI runs, humans steer."
Five Pivots Before Finding the Direction
This direction was not something the team saw from day one. It was found through trial and error.
When CREAO AI launched in September 2025, it was a Vibe Coding tool. By December, the entire product was rebuilt from the ground up around the Agentic App model. But this pivot was just the most recent in a series. Since the team came together in late 2024, they had tried synthetic data, workflow builders, and natural language programming — until the "super agent" approach finally clicked.
"We kept thinking the problem was something specific — data, workflows, code — but every time, we found that the real issue was one layer deeper: how humans and agents actually collaborate," said Clark Gao, co-founder, who leads GTM and previously built data teams at LinkedIn and Tencent.
What makes this pivot story unusual is the density of the team's track record. CTO Peter Pang was a research scientist on the Meta Llama 3 team — one of the most influential projects in open-source AI history — and prior to that, he worked on multimodal model research at Apple. He joined the team with a conviction that others at the time found somewhat exaggerated: "This is only the beginning of a new evolution. The pace will be faster than anyone expects."
Peter has built an AI-First engineering culture at CREAO. In a recent internal memo, he wrote: "AI does not reduce the value of engineers — it changes where that value lies. An engineer's value is no longer measured by how many lines of code they write, but by the clarity of their thinking and the quality of their decisions."
Execution Is the Moat
Cheng Kai has an unusual view on strategy: he believes that for now, strategy is not that important.
"The pace of AI development is astonishing. A direction that looks promising today can be identified and replicated by competitors tomorrow," he said. "Pure conceptual product direction is no longer a defensible advantage."
He used a manufacturing analogy. "Why is it hard for competitors to surpass China in hardware manufacturing? It's not about leading in design concepts — it's about the massive structural efficiency advantages in the supply chain. For AI startups, there is only one real strategic advantage — who can adopt AI first, internalize it, and use it to amplify their own efficiency by 100 times."
CREAO is its own heaviest user. The company's KOL marketing, SEO pipelines, and content production all run on the CREAO platform. A team of fewer than 20 people delivers a workload that would traditionally require several times that many.
One Agent replaced a three-person SEO workflow overnight — keyword research, copywriting, page design, and deployment, all fully automated. Another Agent ran a content pipeline for two days until someone noticed the output was all junk. Both happened in the same week. The team calls themselves the "crash-test dummies of the future of work." That's not entirely a joke.
"Every internal failure becomes a product insight," said Cheng Kai. "Every broken workflow becomes a feature request. We have to become the ultimate AI-driven company internally before we have the right to sell this to the world."
A Hot Sector, Different Bets
The AI agent market is projected to reach USD 52 billion by 2030, with a compound annual growth rate exceeding 46%. A massive amount of capital is pouring in.
Manus was acquired by Meta for USD 2 billion. Genspark completed a USD 275 million Series B at a USD 1.25 billion valuation. Gumloop raised USD 50 million, focusing on no-code enterprise automation. Relevance AI raised USD 24 million, offering pre-configured agent teams.
CREAO AI is riding the same wave, but the bet is fundamentally different. Gumloop lets users drag and drop visual workflows on a canvas; Relevance provides pre-built agent teams. CREAO skips the building interface entirely — no canvas, no drag-and-drop, no node editor. You speak, the agent executes, and the result becomes infrastructure.
"Others are building better ways to command AI," said Cheng Kai. "We are building the layer where AI work compounds — every run feeds memory into the next, and a successful run becomes a self-operating application."
Manus proved that a single powerful agent can complete complex tasks. CREAO is betting on the next step — making that capability durable and repeatable. Genspark built workspaces for knowledge workers. CREAO's workspace is designed for orchestration — not for people to use AI tools, but for people to manage autonomous agents that use the tools for them.
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