Coze Agent Store: The Ultimate Breakthrough for Creator Economy - Ordinary People Can Start AI Business Without Funding

Coze Agent Store: The Creator Economy Meets AI

📖 Glossary

AI Box (also known as Agent Computer / Agent PC), is a dedicated local hardware device that runs AI Agents. Pre-installed with an AI agent management system, plug-and-play, running 24/7. Users can remotely command AI to work via Discord, Slack, Telegram, WhatsApp, and more.

Summary: ByteDance's Coze Agent Store has officially launched, combining zero-code development, multi-platform distribution, and paid subscriptions to enable anyone—regardless of programming skill—to build AI agents and generate recurring revenue. This article dissects the store's mechanics, publishing workflow, monetization paths, and hands-on strategies across four dimensions, offering a comprehensive look at how the AI creator economy is reshaping the Chinese market and beyond.

1. Why the Coze Agent Store Demands Your Attention

When OpenAI launched the GPT Store in early 2024, the global AI community erupted with excitement. The promise was intoxicating: anyone could become an AI application creator. But the GPT Store's reality proved awkward for a massive segment of potential builders. It was designed for English-speaking markets. Chinese users faced access restrictions, and the payment infrastructure was borderline hostile to domestic creators—wire transfers, currency conversion fees, and tax complications made even successful monetization a logistical headache.

The Coze Agent Store fills this gap with surgical precision. Built atop ByteDance's AI agent development platform, Coze (known as 扣子 in Chinese) has targeted the Chinese market from day one. WeChat Mini Programs, Feishu (Lark), Doubao, Douyin—every major domestic channel is natively integrated. Payment and withdrawal require no cross-border gymnastics. For the first time, Chinese-speaking creators have a native AI application monetization platform.

The real opportunity isn't the technology barrier—it's the distribution barrier. Whoever can deliver AI capabilities to the most people wins.

But framing Coze merely as "the Chinese GPT Store" understates its significance. The platform represents a fundamentally different architectural philosophy, one that reflects ByteDance's deep expertise in content distribution and creator economics honed through Douyin and TikTok. To understand why Coze matters, consider its three structural advantages:

First, a genuinely zero-code development environment. Drag-and-drop workflows, visual prompt editors, a built-in plugin marketplace—building an agent with real business logic requires zero lines of code. A marketing operations specialist can spend one afternoon and produce a "Xiaohongshu Viral Title Generator" that actually works. This isn't a toy prototype; the platform's workflow engine supports conditional branching, variable passing, API calls, and database operations. Complex multi-step agent behaviors—like analyzing a legal contract, flagging risky clauses, and generating a revision memo—can be assembled without writing a single function.

Second, one-click multi-platform publishing. The same agent deploys simultaneously to WeChat Mini Programs, Feishu bots, Doubao App, and Douyin private messaging. This isn't merely "convenient"—it directly determines your traffic ceiling. Your agent can reach WeChat's 1.3 billion monthly active users, a distribution advantage no overseas platform can match. Consider the math: even a 0.01% conversion rate from WeChat's ecosystem translates to 130,000 potential users. The same agent on GPT Store would compete for attention among millions of English-language bots, with far less organic reach in Chinese-speaking markets.

Third, deep optimization for Chinese language contexts. Coze's underlying model is Doubao (ByteDance's proprietary LLM), which dramatically outperforms GPT-series models on Chinese semantic understanding, Chinese content generation, and Chinese business scenario adaptation. The gap is especially visible in culturally specific tasks: classical Chinese poetry composition, government document writing, e-commerce customer service with Chinese consumer norms, and legal analysis based on PRC law. These aren't edge cases—they represent the bulk of commercially valuable use cases in the Chinese market.

A fourth, less obvious advantage deserves mention: ecosystem integration. ByteDance controls the full stack—from the foundation model (Doubao) to the development platform (Coze) to the distribution channels (Douyin, Feishu, Toutiao). This vertical integration means Coze can optimize the entire pipeline in ways that fragmented Western ecosystems cannot. When a new Doubao model capability drops, Coze agents get it first. When Douyin's algorithm favors certain content formats, Coze can help agents produce exactly that format.

2. How the Agent Store Works: Mechanics and Incentives

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The Coze Agent Store's core logic is elegantly simple: creators build agents → users discover and use them → creators earn revenue. But beneath this simplicity lies a sophisticated system of incentives, algorithms, and economic mechanisms that reward quality and punish mediocrity.

2.1 Payment Models: Per-Use and Subscription

Coze supports two primary monetization models, each suited to different agent types:

Per-use billing: Users consume credits each time they invoke the agent, and creators receive a proportional share. This model suits tool-type agents—content generators, data analyzers, image processors—where each interaction delivers discrete, measurable value. The per-use model naturally aligns pricing with value delivered; heavy users pay more, casual users pay less, and the creator's revenue scales with actual utility.
Subscription billing: Users pay monthly or annually to unlock the agent's full functionality. This suits continuous-service agents—AI legal advisors, financial assistants, personal coaches—where the relationship between user and agent is ongoing rather than transactional. Subscription models provide predictable recurring revenue, which is far more valuable than per-use income for long-term business planning.

A critical design detail: Coze allows creators to set free trial periods. This is strategically brilliant. Free trials lower the decision barrier for new users, let them experience the agent's value firsthand, and simultaneously filter for users with genuine needs. The psychology mirrors SaaS best practices worldwide—users who've experienced real value are far more likely to convert than those evaluating from the outside.

2.2 Who Sets the Price?

The answer: the creator. You can configure different pricing tiers—Basic at ¥9.9/month, Professional at ¥29.9/month—differentiated by conversation limits, feature scope, response speed, or knowledge base access.

This pricing freedom carries profound implications. An agent's price is determined not by its development cost, but by the severity of the problem it solves. A contract review agent that saves lawyers hours of tedious scanning can charge hundreds of yuan per month and still feel like a bargain. A generic chatbot that offers pleasant but non-essential conversation will struggle to attract users even at zero cost.

This dynamic creates a natural market for quality. Unlike app stores where marketing budgets can compensate for mediocre products, the agent economy rewards genuine utility. Users pay for results, not for polish. A crudely designed agent that consistently delivers accurate legal analysis will outperform a beautifully designed agent that gives vague, unhelpful advice.

2.3 Traffic Distribution: The Algorithm Decides

Coze Store's recommendation logic mirrors Douyin's—personalized recommendations based on user behavior data. Newly listed agents receive cold-start traffic, after which retention and completion rates (here, "task completion rate" and "return visit rate") determine whether the algorithm continues to push them.

This algorithmic distribution model has several important consequences:

Quality compounds. Agents with high completion and return rates get more traffic, which generates more reviews and usage data, which further improves their ranking. The rich get richer.
First-mover advantage is real but not insurmountable. Early agents benefit from less competition during the cold-start phase, but the algorithm's focus on engagement metrics means a genuinely superior later entrant can overtake established agents.
Niche beats broad. Agents that serve specific user segments well tend to have higher retention within those segments than generalist agents trying to serve everyone. The algorithm rewards this focus.

In the age of algorithmic recommendations, good products speak for themselves. But the prerequisite is that you have to be on the shelf first.

2.4 The Revenue Share Question

While Coze has not publicly disclosed its exact revenue-share percentages, industry sources suggest the creator share is competitive with major app stores (typically 70% to creators). The key insight is that ByteDance's business model prioritizes ecosystem growth over platform fees in the early stages—a pattern visible in Douyin's creator monetization programs, which offered generous terms to incentivize content creation before tightening economics once the ecosystem matured. Creators entering the Coze Store now are likely capturing a larger share of revenue than those who will join in two years.

3. From Zero to Published: The Complete Workflow

Many creators stall at the "how do I publish?" step. Coze's publishing process isn't complex, but several critical configurations are easy to misconfigure. Below is a battle-tested checklist drawn from real-world publishing experience.

3.1 Prerequisites and Common Pitfalls

Configuration Item	Description	Common Pitfall
OAuth Token	Used for agent authentication	Token expires without refresh, causing production users to lose access
Bot ID	Unique identifier for the agent	Copying often introduces trailing spaces
Pricing Tiers	Per-use / subscription / hybrid	Start with free trial, then upsell to paid
Free Trial Duration	Duration users can try without paying	Too short = poor conversion; too long = weak payment intent
WeChat Mini Program Domain	Required for WeChat channel deployment	Domain must be added to WeChat backend whitelist

The OAuth token issue deserves special attention. Unlike development environments where a stale token simply means you re-authenticate, a production token expiration means every user attempting to interact with your agent gets an error. Implementing automatic token refresh—or at minimum, setting calendar reminders for manual refresh—is essential for maintaining service reliability.

3.2 Publishing Steps

Complete agent development on the Coze platform: Configure prompts, workflows, knowledge bases, and plugins. Test thoroughly in the preview environment before proceeding.
Configure publishing channels: Select target platforms (WeChat Mini Program, Feishu, Doubao, etc.). Each channel has specific requirements; WeChat demands the most configuration.
Set pricing scheme: Choose billing model, pricing, and free trial duration. Consider your agent's usage pattern—daily-use tools benefit from subscriptions, occasional-use tools from per-use billing.
Submit for review: Coze's team reviews content and compliance, typically taking 1-3 business days. Reviews cover prohibited content, misleading claims, and data handling practices.
Launch and operate: Upon approval, the agent goes live automatically and begins receiving traffic.

3.3 WeChat Mini Program Publishing: Special Considerations

The WeChat channel offers the largest potential audience but requires the most involved configuration:

Register a Mini Program account on the WeChat Open Platform. This involves business verification for commercial agents, which can take several days.
Configure server domain whitelisting (Coze's API domains must be added). WeChat's security model requires explicit whitelisting of all external API endpoints.
Agent name and description must comply with WeChat's naming conventions, which prohibit certain keywords and require accurate representation of functionality.
First-time review may be slow—allow at least one week for the initial submission. Subsequent updates are typically faster.

These hurdles, while annoying, serve an important purpose: they create a quality barrier. Agents that clear WeChat's review process carry implicit credibility, which boosts user trust and conversion rates.

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4. Which Agents Win? Monetization Case Studies

Since the store's launch, several agent categories have demonstrated exceptional commercial performance. The patterns are revealing—not just for what works, but for why it works.

4.1 Content Creation Tools

Case Study: Xiaohongshu Viral Copy Generator

Functionality: Input product information and selling points; the agent automatically generates Xiaohongshu-style promotional copy, including title, body text, hashtags, and emoji. The output mimics the authentic "种草" (planting grass/recommending) tone that performs well on the platform.

Monetization Data: 20,000+ monthly active users, 8% paid conversion rate, approximately ¥30,000 monthly revenue.

Success Factors: - Extreme vertical focus. The agent does one thing and does it exceptionally well—generate copy that performs on one specific platform. It doesn't try to be a general-purpose writing assistant. - Consistent output quality. Users return because the generated copy reliably meets platform expectations. Inconsistent quality would destroy trust instantly. - Freemium sweet spot. The free tier limits users to 3 generations per day—enough to demonstrate value, not enough to satisfy regular needs. This creates natural upgrade pressure without alienating potential users.

The broader lesson: content creation tools succeed when they internalize platform-specific norms. A "social media copy generator" for all platforms produces mediocre output everywhere. A copy generator trained on one platform's conventions can produce genuinely useful results. Depth beats breadth.

4.2 Vertical Domain Professional Assistants

Case Study: Labor Contract AI Review Assistant

Functionality: Upload a labor contract PDF; the AI reviews it clause by clause, flags risky provisions, and provides specific revision suggestions grounded in Chinese labor law.

Monetization Data: Pure subscription model at ¥49/month, 1,000+ paid subscribers, ¥50,000+ monthly revenue.

Success Factors: - Solves a real, painful problem. Lawyer consultations are expensive (typically ¥500-2,000 per hour) and slow (scheduling alone can take days). This agent delivers preliminary analysis in minutes. - Low tolerance for error drives willingness to pay. In legal contexts, a wrong answer has real consequences. Users who might balk at paying for entertainment will readily pay for professional-grade legal analysis. - Knowledge base built on real cases. The agent's effectiveness stems from its curated knowledge base of actual contract disputes and court rulings, not generic legal principles.

Don't try to build an AI assistant that can do everything. Focus on solving one specific problem, and solve it exceptionally well—that's where monetization begins.

This case illuminates a broader principle: the best agents don't replace professionals; they extend their reach. The contract review assistant doesn't replace a lawyer—it handles the initial screening that lawyers find tedious, freeing them for higher-value work. Users get faster, cheaper preliminary analysis. Lawyers get pre-filtered cases. Everyone wins.

4.3 Efficiency Tools

Case Study: Meeting Minutes Auto-Generator

Functionality: Upload a meeting recording or transcript; the agent automatically generates structured meeting minutes including decisions, action items, responsible parties, and deadlines.

Monetization Data: Hybrid per-use + subscription model, approximately ¥15,000 monthly revenue.

Success Factors: - Workplace essential. Every organization needs meeting minutes; few people enjoy producing them. - Quantifiable ROI. Compared to manual summarization, the agent saves approximately 80% of time spent. This makes the value proposition immediately tangible. - Feishu channel distribution. The agent's integration with Feishu (where most meetings in target organizations already happen) creates a natural usage loop: meeting happens in Feishu → recording uploaded to agent → minutes distributed back through Feishu.

The meeting minutes agent demonstrates an important distribution principle: meet users where they already work. An agent that requires users to switch contexts—to open a separate app, copy-paste content, or learn a new interface—faces adoption friction. An agent that integrates into existing workflows eliminates that friction entirely.

4.4 Education and Tutoring

Case Study: IELTS Speaking Practice Agent

Functionality: Simulates the IELTS speaking test environment; the AI acts as an examiner, asks questions, and provides scoring and improvement suggestions based on the student's responses.

Monetization Data: Subscription model at ¥29/month, 3,000+ paid subscribers, ¥80,000+ monthly revenue—the highest-revenue case in our analysis.

Success Factors: - Education sector's strong payment willingness. Chinese families spend heavily on education—IELTS preparation alone represents a multi-billion yuan market. A ¥29/month tool is trivially inexpensive compared to ¥300-500/hour foreign tutor sessions. - High-frequency need. Speaking practice requires daily repetition for improvement, creating consistent engagement and low churn. - Dramatic cost advantage over alternatives. AI practice costs roughly 1/10th to 1/20th of human tutor rates, making it accessible to a far larger market segment.

This case reveals perhaps the most lucrative pattern in the agent economy: democratizing expensive professional services. When an agent can deliver 70-80% of a human professional's effectiveness at 5-10% of the cost, the market opportunity is enormous. The IELTS agent doesn't replace expert tutors for advanced students—but it captures the massive middle market that can't afford regular human instruction.

4.5 Cross-Category Patterns

Looking across these cases, three structural patterns emerge:

Vertical depth beats horizontal breadth. Every successful agent solves a specific problem for a specific audience. General-purpose agents struggle to justify payment because their output, while broadly useful, isn't indispensable for any single use case.
Pricing power comes from pain severity. The IELTS agent and the legal review agent both command premium pricing because the problems they solve are genuinely painful for their users. Nobody enjoys contract anxiety or IELTS preparation stress.
Distribution integration drives adoption. The most successful agents leverage their publishing channels as more than mere access points—they integrate into platform workflows to create seamless user experiences.

5. Practical Strategies for New Creators

5.1 Choosing Your Direction: Find "Three-High" Scenarios

Before building anything, rigorously evaluate potential agent ideas against three criteria:

High frequency: Users need this daily or weekly (content writing, meeting summaries, customer service). High frequency drives habit formation, which drives retention, which drives revenue.
High pain: Not using the solution causes genuine suffering (legal review, tax calculation, compliance checking). Pain creates urgency, and urgency creates willingness to pay.
High existing spend: Users already spend money in this domain (training, consulting, outsourcing). Pre-existing payment behavior is the strongest predictor of willingness to pay for an AI alternative.

Meeting two of three criteria makes a project worth pursuing. Meeting all three signals a golden opportunity. Let's apply this framework:

Agent Idea	Frequency	Pain	Existing Spend	Verdict
IELTS speaking practice	✅ Daily	✅ High (test anxiety)	✅ ¥300-500/hr tutors	🏆 Golden
Contract review	⚠️ Occasional	✅ High (legal risk)	✅ ¥500-2,000/hr lawyers	✅ Strong
Meeting minutes	✅ Weekly+	⚠️ Moderate (annoying)	⚠️ Intern time	✅ Good
General chatbot	✅ Daily	❌ Low	❌ None	❌ Weak

5.2 Building the Product: MVP First, Iterate Always

Don't pursue perfection on day one. Build the simplest possible workflow that delivers your agent's core value, publish it to the store, collect real user feedback, and iterate based on data.

A common and costly mistake: spending a month polishing features, only to discover after launch that nobody wants the product. The MVP approach—validate demand first, optimize experience second—applies with particular force in the agent economy, where development costs are low but opportunity costs of delayed launch are high.

The Coze platform's visual workflow builder actually encourages MVP thinking. You can assemble a functional agent in hours, not weeks. This dramatically shortens the feedback loop: build → launch → measure → learn → rebuild. Each cycle costs hours, not months.

Practical MVP guidelines: - Start with a single workflow that delivers your core value proposition - Use Coze's built-in plugins rather than building custom integrations - Set up the simplest viable pricing (free trial → single paid tier) - Publish and collect data for 2-4 weeks before adding features

5.3 Driving Conversion: The Art of Free Trials

Free trial duration directly impacts conversion rates. Based on observed data across the store:

Tool-type agents: 3-5 free uses. Enough for users to experience the value, not enough to satisfy regular needs. The "itch but can't scratch" feeling drives conversion.
Service-type agents: 1-3 day free trials. Sufficient to complete one full experience cycle (e.g., one IELTS practice session with feedback).
Post-trial payment prompts should feel natural, not aggressive. "Continue with full access" outperforms "Pay now or lose access."

An underappreciated nuance: the free trial is also a quality filter. Users who try your agent and don't convert may not be your target audience. Better to let them self-select out during the trial than to acquire them as paying users who churn in month one—churned users damage your algorithmic ranking and generate negative reviews.

5.4 Sustained Operations: Making Your Agent Work 24/7

Publishing is just the beginning. Agents require continuous maintenance: updating knowledge bases, refining prompts based on edge cases, responding to user feedback, and monitoring performance metrics.

Here's a crucial insight: an agent's greatest advantage is that it never sleeps. A well-designed agent can generate proposals at 3 AM, process consultations on weekends, and serve customers across time zones—all without human intervention. This is the fundamental promise of the Agent Computer.

Ordinary people sell their time—24 hours per day maximum. Agents sell services—24/7 without interruption. This isn't incremental efficiency improvement; it's a qualitative transformation of the income model.

This is precisely the philosophy behind KaiheAiBox. When your Coze agent automatically acquires customers, delivers service, and collects payment in the store, it functions as a tireless Agent Computer—a digital worker that operates around the clock, scaling your expertise and time far beyond personal limits. KaiheAiBox extends this principle further: it's designed to make Agent Computers accessible even to those without technical expertise, ensuring that the 24/7 economy isn't limited to developers and engineers. Whether you're a lawyer, a teacher, a marketer, or a consultant, KaiheAiBox provides the infrastructure for your expertise to work continuously as an agent.

6. The Bigger Picture: AI Creator Economy Fundamentals

The Coze Agent Store isn't an isolated phenomenon—it's the leading edge of a structural shift in how software is created, distributed, and monetized. Understanding the underlying economics helps explain why this shift is happening now and where it's headed.

6.1 From Code to Prompts: The Development Cost Collapse

Traditional software development requires specialized skills (programming), significant time (weeks to months), and substantial investment (developer salaries, infrastructure). The Coze platform replaces code with prompts and visual workflows, reducing development time from weeks to hours and eliminating the programming skill requirement entirely.

This isn't merely a quantitative improvement—it's a qualitative shift. When development costs approach zero, two things happen:

The set of people who can create software expands dramatically. The addressable creator population grows from millions of developers to hundreds of millions of domain experts.
The economics of niche applications flip. Previously, an agent serving a vertical market of 10,000 users wasn't worth building because development costs exceeded potential revenue. At near-zero development cost, that same agent becomes profitable.

6.2 From Downloads to Conversations: The Distribution Revolution

Traditional software distribution relies on app store search, SEO, and paid acquisition—channels that favor well-funded companies. Agent distribution, by contrast, flows through conversational interfaces and algorithmic recommendation. The Coze Store's Douyin-style recommendation algorithm can surface niche agents to precisely the users who need them, at zero marketing cost to the creator.

This shift has profound implications for market structure. In traditional app stores, the top 1% of apps capture 94% of revenue. In agent stores with algorithmic distribution, the long tail may be significantly fatter—because the algorithm can profitably recommend agents to micro-audiences that would be invisible to traditional marketing.

6.3 From Products to Services: The Monetization Shift

Traditional software is sold as a product—one-time purchase, feature-complete. Agents are sold as services—ongoing subscription, continuously improving. This shift aligns creator incentives with user outcomes: creators earn more when users continue finding value, which incentivizes continuous improvement.

The subscription model also enables a new form of compounding: as an agent accumulates more user interactions, its knowledge base and prompt refinements improve, which attracts more users, which generates more interaction data. This feedback loop creates natural moats—early movers who iterate aggressively can build quality advantages that late entrants struggle to match.

7. Risks, Challenges, and Honest Caveats

Objectively, the Coze Agent Store is not a goldmine where everyone strikes it rich. Several realities demand clear-eyed acknowledgment.

Homogenization and commoditization. Low barriers mean fast copying. Build a successful copy generator today, and fifty imitators will list tomorrow. Sustainable differentiation requires either domain depth (exclusive knowledge bases, proprietary data pipelines) or network effects (user communities, collaborative features). Without a moat, agents become commodities competing solely on price—a race to the bottom.

Platform dependency. Revenue depends entirely on Coze's distribution algorithms and platform rules. Algorithm adjustments, commission rate changes, or tightened review policies can directly and immediately impact your income. This is the same lesson Shopify merchants and YouTube creators learned: platform businesses offer extraordinary reach but also extraordinary dependency. Prudent creators diversify across channels and build direct user relationships where possible.

User retention and the novelty cliff. Agents suffer from rapid "novelty decay." Users try a new agent, find it impressive, and then... stop using it. If the core functionality doesn't deliver sustained, increasing value, paid users churn in month two. The solution is continuous updates, continuous iteration, and genuine engagement with user feedback. Agents are not "build once, earn forever" products—they're living services that require ongoing investment.

Compliance and liability risks. Agents operating in regulated domains—healthcare, law, finance—face compliance red lines. AI-generated content that provides incorrect advice causing user harm exists in a legal gray zone regarding liability attribution. Chinese regulators are actively developing AI governance frameworks; agents in sensitive domains should implement clear disclaimers, human-in-the-loop checkpoints, and robust content filtering. The legal landscape is evolving rapidly—what's acceptable today may not be tomorrow.

The quality-quantity tension. The store's success depends on attracting quality agents, but low barriers inevitably produce large volumes of low-quality submissions. If users encounter too many mediocre agents, trust in the platform erodes, harming even high-quality creators. Coze must navigate this tension carefully—stringent review processes improve average quality but reduce supply velocity, while permissive review grows supply but risks user experience degradation.

Market timing risk. The AI agent market is nascent and rapidly evolving. Technologies, user expectations, and platform capabilities are all changing at breakneck speed. An agent built today may be rendered obsolete by platform updates, model improvements, or competitive entrants within months. Creators must build with adaptability in mind—modular workflows, decoupled knowledge bases, and prompt architectures that can be quickly updated without full rebuilds.

8. The Global Context: Coze vs. GPT Store vs. What Comes Next

Understanding Coze's position requires situating it within the broader global agent economy landscape.

8.1 The GPT Store: Lessons from the Pioneer

The GPT Store launched with enormous hype but has struggled to deliver sustained value for creators. Key issues include:

Discovery problems. With hundreds of thousands of GPTs, discovery is dominated by a small number of featured agents. Long-tail creators struggle to find audiences.
Weak monetization. OpenAI's revenue sharing has been limited and inconsistent. Many creators report negligible earnings despite significant usage.
Quality dilution. Low barriers combined with weak curation have produced a glut of low-quality GPTs that erode user trust in the platform.

Coze benefits from observing these failures and designing accordingly—but it must also avoid repeating them as it scales.

8.2 The Chinese Market Advantage

The Chinese market offers structural advantages for agent economy growth that are often underestimated by Western observers:

Mobile-first user behavior. Chinese consumers are far more comfortable conducting complex transactions through mobile apps (WeChat Mini Programs, in particular) than through desktop web interfaces. This aligns perfectly with Coze's distribution strategy.
Mature creator economy infrastructure. Years of Douyin, Kuaishou, and Xiaohongshu creator ecosystems have built a large population of creators who understand content monetization, audience engagement, and algorithmic distribution. These skills transfer directly to agent creation.
Payment friction elimination. WeChat Pay and Alipay have eliminated payment friction to a degree unmatched in most markets. Users who wouldn't enter credit card details for a $5 subscription will seamlessly scan a QR code for ¥29.

8.3 What's Coming Next

Several developments will shape the agent economy's trajectory over the next 2-3 years:

Multi-agent collaboration. Individual agents will compose into multi-agent workflows, where a legal review agent passes results to a contract drafting agent, which routes to a compliance checking agent. Platforms that enable this composition will unlock exponentially more value than single-agent stores.
Agent-to-agent markets. Agents will not only serve end users—they'll serve other agents. A "market research agent" might be invoked by a "strategy planning agent" as part of a larger workflow. This creates a B2B layer within the agent economy.
Personalized agent fine-tuning. As models become more efficient at fine-tuning, agents will increasingly adapt to individual user preferences and history, creating personalized experiences that justify premium pricing.
Cross-platform agent portability. Agents currently locked to specific platforms (Coze, GPT Store, etc.) will become portable, running on any compatible runtime. This shift will reduce platform dependency and empower creators—but it will also increase competition.

KaiheAiBox is positioned at the intersection of these trends, building the Agent Computer infrastructure that enables agents to run continuously, collaborate effectively, and scale without limits. As the agent economy evolves from novelty to necessity, the hardware and platform layer that supports 24/7 agent operation becomes as critical as the agents themselves.

9. Getting Started Today: A Concrete Action Plan

For creators ready to enter the Coze Agent Store, here's a structured approach:

Week 1: Validate Your Idea

Identify 3-5 potential agent concepts using the "Three-High" framework
For each concept, find and test existing agents on the store. What's missing? What's poorly executed?
Talk to 5-10 potential users. Don't pitch your idea—ask about their current workflow and pain points.

Week 2: Build Your MVP

Select the strongest concept from Week 1
Build a single-workflow agent on Coze that delivers the core value proposition
Keep it simple: one workflow, one pricing tier, minimal configuration
Test thoroughly in the preview environment

Week 3: Launch and Learn

Configure publishing channels (start with Doubao—fastest to deploy)
Set a 3-day free trial with a single paid tier
Submit for review
Upon approval, monitor metrics daily: trial signups, conversion rate, task completion rate

Week 4: Iterate Based on Data

Analyze user feedback and usage patterns
Identify the #1 improvement that would increase task completion rate
Implement and redeploy
Consider adding a second distribution channel (WeChat or Feishu)

Month 2+: Scale What Works

If conversion rate exceeds 5%, invest in refinement and additional features
If conversion rate is below 3%, pivot—either refine the value proposition or switch to a different agent concept
Build direct user relationships (WeChat groups, feedback channels) to reduce platform dependency
Consider adding a premium tier for power users

10. Closing Perspective

The Coze Agent Store's launch marks a substantive milestone for the Chinese AI creator economy. Unlike the GPT Store, it requires no VPN, no dollar-denominated account, no English-language content. For the first time, Chinese internet creators have a native AI application monetization platform—complete with domestic distribution, domestic payment, and domestic language optimization.

But opportunity windows don't stay open forever. The first wave of high-quality agents is already capturing algorithmic recommendation dividends. As store content saturates, new entrants' customer acquisition costs will rise significantly. The pattern mirrors every platform economy: early builders who establish quality and reputation during the growth phase enjoy compounding advantages that latecomers struggle to overcome.

The best time to plant a tree was ten years ago. The second best time is now. The best time to list your agent was yesterday. The second best time is today.

If you have an idea, don't hesitate—open Coze, build the simplest viable version, and publish. The Agent Computer era has arrived. Your 24/7 digital workforce starts today.

KaiheAiBox · AI Agents