The Agent Store Has Arrived: From "Building for Fun" to "Building for Profit"—An Ordinary Person's Guide to Getting In
Abstract: ByteDance's Coze platform has launched its Agent Store, featuring zero-code creation, template replication, and monetization tools like BotCash—the AI agent creator economy is sprouting. But the path from "toy" to "tool" to "product" is far more winding than imagined. Monetization remains the biggest pain point, and competition is already fiercely crowded. Can ordinary people really earn over 10,000 yuan per month by building AI agents? Let's break it down.
The "App Store Moment" for AI Agents
In 2008, Apple's App Store launched, opening the mobile app developer era. Countless ordinary people achieved financial freedom through app development—or at least, that's how the story goes.
In 2026, Coze's Agent Store went live. The same grand promise is being drawn: zero-code agent creation, one-click publishing, automatic distribution, and passive income.
Is this the "App Store moment" for AI agents?
The answer: both yes and no. Yes, because it genuinely lowers the barrier for ordinary people to participate in the AI economy. No, because there's a fundamental difference between agents and apps—and this difference determines that the creator economy's shape will be entirely different.
Why Agents ≠ Apps
This distinction is crucial and often overlooked. Apps are deterministic software—the same app performs consistently across different devices. Agents are non-deterministic software—the same agent may give completely different answers in different conversations.
This non-determinism has profound implications for: - Quality assurance: You can't guarantee an agent will always work correctly - Pricing: How do you price something that works 90% of the time? - Liability: Who's responsible when an agent gives bad advice? - User expectations: Users expect software to work; they don't expect software to sometimes work
The App Store model succeeded partly because apps are predictable products. The Agent Store model must grapple with fundamentally unpredictable products. This is why the creator economy for agents will look very different from the app economy.
The Three-Stage Evolution of AI Agents
Stage One: Toy (2023-2024)
Early AI agents were mostly "build for fun" products—a chatbot that tells jokes, a template that helps write weekly reports, a Bot that imitates celebrity speech patterns.
This stage's defining characteristic: fun but useless. Users enjoyed the novelty, then forgot about it after two days. Without genuine demand driving usage, there was naturally no willingness to pay.
Yet the "toy" stage served an important purpose: it got millions of people comfortable with the idea of interacting with AI agents. This user education was essential preparation for the stages that followed.
Stage Two: Tool (2024-2025)
As large model capabilities improved and platform toolchains matured, agents began solving real problems:
- Customer service agents: Handling common questions 24/7
- Writing assistants: Batch-generating marketing copy
- Data analysis bots: Automatically organizing reports
- Translation assistants: Real-time multilingual translation
This stage's defining characteristic: useful but unreliable. Agents work correctly 80% of the time but fail 20% of the time. For serious business scenarios, this failure rate is still too high.
The 80/20 reliability ratio created a peculiar dynamic: businesses wanted to use agents but couldn't fully trust them. This led to the "human-in-the-loop" pattern—agents do the initial work, humans review and approve. Better than nothing, but far from the autonomous ideal.
Stage Three: Product (2025-Present)
The Coze Agent Store's launch marks the beginning of the third stage: agents are beginning to be traded as "products."
But "productization" is far more complex than imagined. Apps are deterministic software—the same app performs consistently across different phones. Agents are non-deterministic software—the same agent may give completely different answers in different conversations. This non-determinism is the biggest barrier to productization.
However, some agent categories are more amenable to productization than others: - Information retrieval agents (search, summarize, translate): Relatively deterministic - Creative agents (writing, brainstorming): Inherently variable, but variability can be a feature - Decision-making agents (investment advice, medical diagnosis): Non-determinism is dangerous
The smartest creators focus on categories where non-determinism is tolerable or even desirable, avoiding categories where reliability is non-negotiable.
Deep Dive into Coze Agent Store
Core Features
Coze Store provides a complete creator toolchain:
Zero-Code Creation. Build agents through a visual interface by dragging and dropping components—no coding required. Supports multiple component types: LLM invocation, knowledge base integration, tool calling, workflow orchestration.
The zero-code approach has democratized agent creation in a way that coding-based platforms couldn't. A marketing professional who's never written a line of Python can now create a custom agent that understands their brand guidelines, target audience, and content strategy.
Template Store. Hundreds of pre-built templates covering customer service, writing, translation, data analysis, and other scenarios. Creators can modify existing templates or publish their own templates for others to use.
Templates serve as both learning tools and productivity multipliers. New creators can study successful templates to understand what works, then adapt those patterns to their own niches. This dramatically reduces the iteration time from idea to functional agent.
One-Click Publishing. Finished agents can be published to the store with one click, supporting pricing models (free/paid/subscription).
Analytics Dashboard. Provides usage metrics, revenue data, user feedback, and other analytics.
The analytics are surprisingly sophisticated. Creators can see: - How many conversations their agent handles daily - Where users abandon conversations (indicating failure points) - Which features are most used - Revenue trends and projections - User satisfaction scores
Monetization Models
Coze Store currently supports three monetization models:
- One-Time Purchase: Users pay a single fee for permanent access to the agent
- Pay-Per-Use: Users pay each time they invoke the agent, similar to API calls
- Subscription: Users pay monthly/yearly for ongoing access
Each model has trade-offs:
| Model | Advantage | Disadvantage | Best For |
|---|---|---|---|
| One-time purchase | Certain revenue | Hard to sustain | Tool-type agents |
| Pay-per-use | Pay for what you use | Unstable income | Analysis-type agents |
| Subscription | Recurring revenue | Requires ongoing updates | Service-type agents |
Pricing Challenges Unique to Agents:
Pricing AI agents is fundamentally harder than pricing apps because: - The marginal cost per user is not zero (each interaction consumes LLM API tokens) - Value delivery is variable (some interactions are highly valuable, others worthless) - Competition includes free alternatives (anyone can prompt GPT directly)
The most successful creators have found pricing models that align with value delivery rather than simple access. For example, a legal document analysis agent charges per document analyzed rather than per month of access—users pay for outcomes, not access.
BotCash: Third-Party Monetization Tools
Beyond Coze's official monetization channels, third-party tools like BotCash have emerged:
- Agent Management Services: Helping creators optimize agent performance and pricing
- Traffic Connection: Integrating agents into enterprise clients' actual business processes
- Deep Analytics: Analyzing user behavior to optimize agent design
BotCash's emergence signals something important: the agent creator economy's supply chain is forming, but official channels' monetization efficiency isn't yet high enough, requiring intermediaries to bridge supply and demand.
The intermediary layer is actually a positive sign for the ecosystem's health. It means: 1. There's enough demand to support specialized services 2. The market is complex enough that simple direct-to-consumer models don't always work 3. Professional specialization is occurring (some people are better at building agents; others are better at selling them)
Platform Competition: Coze vs. Dify vs. FastGPT
The agent platform space isn't Coze's alone.
Coze
- Backed by: ByteDance
- Strengths: Traffic entry points (Douyin/Feishu ecosystem), excellent zero-code experience, rich templates
- Weaknesses: Platform lock-in, limited customization, commercialization still exploring
- Best for: Non-technical users, rapid idea validation
Coze's biggest advantage is ByteDance's distribution. When you can push an agent to millions of Douyin users, adoption happens fast. But this distribution comes with strings attached—agents live within ByteDance's ecosystem, subject to its rules and its revenue sharing.
Dify
- Backed by: Independent startup, venture-funded
- Strengths: Open-source, high technical freedom, supports private deployment
- Weaknesses: Requires technical background, community smaller than Coze
- Best for: Technical users, enterprise-grade applications
Dify's open-source approach means no platform lock-in. You can take your agent and run it anywhere. But this freedom comes with responsibility—you handle your own hosting, scaling, and maintenance.
FastGPT
- Backed by: Open-source community driven
- Strengths: Completely free, lightweight, strong knowledge base capabilities
- Weaknesses: Limited features, incomplete ecosystem
- Best for: Individual users, Q&A scenarios
FastGPT fills an important niche: the simplest possible path from "I have some documents" to "I have a Q&A bot." No bells and whistles, but it works reliably for knowledge retrieval.
The Competitive Landscape
Three platforms represent three different paths: Coze takes the "big platform + low barrier" route, Dify takes the "open source + technical freedom" route, FastGPT takes the "minimalist + free" route. In the short term, no winner-takes-all scenario will emerge; each will have its own user base.
What's missing from the current landscape is a platform that combines Coze's ease of use with Dify's openness and FastGPT's simplicity. This gap represents an opportunity—either one of these platforms will evolve to fill it, or a new entrant will emerge.
The Truth About Monetization: Is 10,000+ Yuan Per Month Really Possible?
The Optimistic Reality
People are indeed making money. Coze Store's top creators have monthly revenues exceeding 10,000 yuan, primarily from:
- Enterprise customer service agents: Monthly subscription revenue of 5,000-8,000 yuan
- Marketing copy generators: Pay-per-use, monthly revenue of 3,000-5,000 yuan
- Industry knowledge Q&A bots: One-time purchase + upgrade fees, monthly revenue of 2,000-4,000 yuan
These numbers come from Coze's own data releases and creator interviews. They're real, but they paint an incomplete picture.
The Pessimistic Reality
But the power-law distribution is extremely pronounced. Coze Store data shows:
- Top 10% of creators capture 80% of revenue
- 50% of creators earn less than 200 yuan per month
- Average agent lifespan is only 2-3 months
Let's put this in perspective. If you're a new creator entering the store today: - You're competing against thousands of existing agents - You have no established reputation or user base - Your agent will likely be replicated or improved upon within weeks - You'll probably earn less than a part-time barista for the first several months
Earning over 10,000 yuan monthly isn't impossible, but for the vast majority, it's like "earning over 10,000 yuan making apps"—survivorship bias makes the few successes look like the norm.
Why Monetization Is Hard
Three core reasons:
First, fragmented demand. Agents solve problems in niche scenarios, but each niche's user base is small. Unlike apps that can profit through scale effects, agents have a very low ceiling on paying users.
A weather app can serve millions of users with the same code. A "pharmaceutical regulatory compliance agent" might serve 50 companies total. The development effort is similar; the revenue potential is vastly different.
Second, easy substitution. A customer service agent's functionality can be replicated by switching platforms or tweaking prompts. No technical moat means price wars, and price wars mean profits approach zero.
The replication problem is exacerbated by the nature of LLMs. When a creator discovers an effective prompt pattern or workflow, it's trivially easy to copy. Unlike traditional software where the code itself is the moat, agent behavior is defined by prompts and configurations that are essentially transparent.
Third, uncontrollable quality. Agent output quality depends heavily on the underlying LLM, and creators have limited control over quality. When LLMs upgrade or downgrade, your agent's performance may swing dramatically.
This creates a unique risk for agent creators: your product's quality can change overnight without any action on your part. A model update that improves general capabilities might break specific behaviors your agent relied on. This uncertainty makes long-term commitments to agent businesses risky.
The Mathematics of Agent Revenue
Let's do some honest math. For a moderately successful agent:
- Monthly active users: 500
- Conversion rate to paying users: 5% (25 paying users)
- Average monthly revenue per paying user: 50 yuan
- Monthly revenue: 1,250 yuan
- Platform commission (typically 30%): -375 yuan
- Net monthly revenue: 875 yuan
To reach 10,000 yuan monthly: - You'd need either 200 paying users at 50 yuan each, or - 50 paying users at 200 yuan each, or - Some combination in between
Both scenarios require either a large user base (hard to acquire) or high per-user revenue (hard to justify). This is why most creators supplement agent sales with related services.
Four Paths for Ordinary People to Enter
Path One: Template Business
Don't sell agents themselves; sell the methodology for "how to build agents." Publish high-quality templates in the template store for other creators to use.
Advantages: Build once, earn continuously Disadvantages: Templates are easily imitated Expected monthly revenue: 500-3,000 yuan
The template business works because of information asymmetry: experienced creators know patterns that newcomers don't. A well-structured template that incorporates best practices for prompt engineering, workflow design, and error handling is worth paying for—if you're saving hours of trial and error.
Path Two: Vertical Domain Expert
Choose a vertical domain you're familiar with (law, healthcare, education, e-commerce) and build domain-specific agents. Professional knowledge barriers are much higher than technical capability barriers.
Advantages: Expertise barriers are hard to replicate Disadvantages: Requires domain expertise Expected monthly revenue: 1,000-8,000 yuan
This is where the real money is. A lawyer who builds a contract review agent brings domain expertise that no amount of prompt engineering can replicate. The agent's value comes from the lawyer's knowledge, not from the underlying LLM.
The key insight: in the agent economy, domain expertise is the moat, not technical skill. Anyone can learn to use Coze. Very few people understand pharmaceutical regulatory compliance.
Path Three: Management Services
Help traditional businesses that don't understand AI to build and maintain agents. You're selling services, not agents.
Advantages: High client stickiness, stable income Disadvantages: Labor-intensive, hard to scale Expected monthly revenue: 3,000-15,000 yuan
The service model is surprisingly robust because: - Small businesses need AI but don't know where to start - Custom agent development requires ongoing maintenance - The relationship between service provider and client creates natural lock-in - You can charge premium rates for domain-specific customization
Path Four: Agent + AI Computer Solutions
Deploy agents on 24/7 online AI computers, providing "Agent-as-a-Service" for businesses. You're not selling software; you're selling continuously running capability.
Advantages: Differentiated competition, high value-added Disadvantages: Requires technical integration capability Expected monthly revenue: 5,000-20,000 yuan
This is the most promising path for significant revenue. When an agent runs on a dedicated AI computer: - It's always available (no cold starts) - It can handle scheduled tasks (daily reports, weekly summaries) - It provides consistent service quality - It can integrate with multiple tools and data sources
The difference between "an agent that works when you talk to it" and "an agent that works for you 24/7" is the difference between a tool and a service. Services command higher prices and create deeper customer relationships.
The people who make the most money aren't those who build agents—they're those who help others turn agents into productivity.
The Creator Economy's Structural Challenges
Beyond individual monetization challenges, the agent creator economy faces structural issues:
Platform Dependency
Creators are entirely dependent on platform decisions. If Coze changes its revenue sharing model, algorithm, or terms of service, creators have little recourse. This is the same problem that plagued mobile app developers and YouTube creators.
Mitigation strategies: - Build on multiple platforms simultaneously - Maintain direct relationships with customers outside the platform - Focus on portable skills (domain expertise) rather than platform-specific capabilities
The Race to the Bottom
As more creators enter the market, pricing pressure increases. Free agents set the baseline; paid agents must justify their premium. This leads to a "race to the bottom" where only the most differentiated agents can sustain premium pricing.
The Update Problem
Agents require ongoing maintenance: - LLM updates may change agent behavior - User feedback reveals edge cases - Competitors release better alternatives - Business requirements evolve
Creators who treat agents as "build once, sell forever" products will be disappointed. Successful agent businesses are more like consulting practices—requiring ongoing client engagement and continuous improvement.
Lessons from the App Economy
The app economy offers instructive parallels and warnings:
What's Similar
- Long-tail distribution of revenue (few winners, many losers)
- Platform gatekeeping (App Store → Agent Store)
- Race to the bottom on pricing
- Importance of marketing and distribution, not just product quality
What's Different
- Apps are deterministic; agents are probabilistic
- Apps have clear feature sets; agents have fuzzy capability boundaries
- App development requires coding; agent creation can be zero-code
- Apps compete on features; agents compete on prompt quality and domain expertise
What We Can Learn
From the app economy's 16-year history, the most important lesson is: sustainable businesses are built on services, not products. The most successful app developers aren't those who sold the most downloads—they're those who built recurring revenue through subscriptions, in-app services, or ecosystem businesses.
The same principle applies to agents. One-time agent sales will rarely generate significant revenue. Subscription models, service agreements, and ongoing consulting relationships are where the real money is.
The Future of Agent Creator Economy
Looking ahead, several trends will shape the agent creator economy:
Trend 1: Specialization
General-purpose agents will be dominated by big platforms. Creator opportunities lie in specialized verticals where domain expertise creates genuine moats.
Trend 2: Agent-as-a-Service
The shift from selling agents to selling agent-powered services. Instead of "buy my customer service agent," it's "I'll handle your customer service using my agent." This aligns incentives and justifies premium pricing.
Trend 3: Multi-Agent Systems
Single agents have limited capabilities. The future belongs to multi-agent systems where specialized agents collaborate. Creators who can orchestrate these systems will command premium prices.
Trend 4: Agent Marketplaces Beyond Platforms
As the ecosystem matures, independent agent marketplaces may emerge—less controlled by any single platform, more like decentralized exchanges for agent capabilities.
Trend 5: Regulation and Liability
As agents handle more consequential tasks (financial advice, medical information, legal guidance), regulation will inevitably follow. Creators who proactively address compliance will have a competitive advantage.
Final Thoughts
The agent creator economy is sprouting—that's a fact. But between "sprouting" and "flourishing" lies a long validation period.
When the App Store launched in 2008, everyone thought they could strike it rich making apps. Sixteen years later, we know: the truly profitable ones are an extremely small minority; most developers can't even cover their server costs. The agent creator economy will likely replay this story—but probability doesn't equal destiny, and opportunity always favors those who understand the rules and find the right path.
If you're ready to enter, remember three principles:
- Don't go generic, go vertical—generic agents can't compete with big platforms; vertical domains win through professional knowledge
- Don't sell software, sell services—agents are non-deterministic products; only services can guarantee outcomes
- Don't just look at revenue, look at renewal—one-time transactions aren't valuable; sustained payment is the real value
Agents are tools; AI computers are the infrastructure that keeps tools running continuously. Without a 24/7 online runtime environment, even the best agent is just a demo. When agents evolve from "demonstrations" into "productivity," the creator economy truly becomes viable.
The agent store is open. The question isn't whether there's opportunity—it's whether you have the patience, the domain expertise, and the service mindset to capture it.
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