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What Should AI Agents Build to Make Money?

Cowrie
Cowrie
Dev @ Bswen

The Problem

I watched an AI agent experiment unfold on Reddit. Someone gave autonomous agents $26.48 to start—a domain name and a marketplace account. The goal: build products that generate enough revenue to cover LLM API costs.

Week 1 results:

  • 22 free downloads
  • 5-star reviews
  • $0 revenue

The comment that cut to the chase:

“Right now it sounds like the agents built tools for people building agents. That market is extremely crowded.”

The agents successfully built three products in a week. They worked flawlessly. But they built the wrong things.

The Trap: Building for AI Builders

AI agents naturally gravitate toward building developer tools. I see this pattern repeatedly:

  1. The builders are developers who understand dev problems
  2. AI agents are trained on technical documentation
  3. The AI ecosystem is self-referential—builders building for builders

This creates a feedback loop where:

  • Distribution becomes the bottleneck, not product quality
  • Free alternatives exist for most tools
  • The target audience (developers) expects free tools

The Reddit experiment proved this: a free starter kit with 22 downloads and 5-star reviews generated exactly $0.

The Builder Trap
Input:    AI agents trained on code, docs, and dev content
Output:   Developer tools, API wrappers, starter kits
Result:   Oversaturated market with zero willingness to pay


Why?      Developers expect free tools
          Distribution is harder than building
          Every AI builder has the same idea

Where Money Actually Flows

The Reddit community pointed to a better direction. One comment stood out:

“If I ran a team of agents I would point them at problems where people already spend money instead of dev tools.”

This reframed the entire problem. The question isn’t “what can AI agents build?” but rather “where do people already pay for solutions?”

I identified three markets where payment behavior exists:

Revenue-Ready Markets
Market                  Payment Behavior          Agent Opportunity
---------------------------------------------------------------------------
Local Business          Businesses pay for        Scrape directories,
Lead Generation         leads (Yelp, Google)      build landing pages


E-commerce              People buy "dumb stuff"   Design generation,
Automation             (stickers, t-shirts)      product listings


Specialized B2B         Companies pay for         Document processing,
Services               efficiency gains          compliance checking

Let me break down each market.

Market 1: Local Business Lead Generation

Local businesses already pay for leads. They pay Yelp, Google Ads, HomeAdvisor, and directories. The payment behavior exists.

Why this works:

Lead Generation Economics
Customer:      Local businesses (dentists, plumbers, restaurants)
Payment proof: Yelp charges $300-600/month for leads
               Google Ads CPC: $10-100+ for local keywords
               HomeAdvisor: $15-60 per lead


Agent tasks:   Scrape niche directories
               Build simple landing pages
               Automate outreach emails
               Track responses


Revenue model: Monthly subscription ($50-200/month)
               Per-lead pricing ($5-50/lead)

What the agent does:

lead_generation_agent.py
from dataclasses import dataclass
from typing import Optional


@dataclass
class Business:
    name: str
    niche: str
    location: str
    contact: Optional[str]
    website: Optional[str]


class LeadGenerationAgent:
    def __init__(self, niche: str, location: str):
        self.niche = niche
        self.location = location
        self.prospects: list[Business] = []


    async def find_prospects(self) -> list[Business]:
        """
        Scrape local directories for businesses without
        strong online presence.
        """
        # Identify businesses with:
        # - No website or poor website
        # - Missing Google Business Profile
        # - Low review counts
        ...


    async def build_landing_page(self, business: Business) -> str:
        """
        Generate simple landing page with lead capture.
        """
        # Template-based page generation
        # Include contact form
        # Auto-deploy to hosting
        ...


    async def automate_outreach(self, prospects: list[Business]) -> int:
        """
        Send personalized emails offering services.
        """
        # Template emails with personalization
        # Follow-up sequences
        # Track responses
        ...

The key insight: local business owners aren’t technical. They don’t compare your solution to free alternatives on GitHub. They pay for results.

Market 2: E-commerce Automation

People buy products online. Lots of products. Even “dumb” products like stickers, t-shirts, and custom mugs.

A Reddit commenter put it bluntly:

“Make stickers or t-shirts, people buy dumb stuff.”

This sounds dismissive, but it’s accurate. E-commerce is a massive market where payment behavior is normalized.

Why this works:

E-commerce Automation
Customer:      General consumers
Payment proof: Sticker market: $150M+ in US alone
               Print-on-demand platforms: 100M+ products sold


Agent tasks:   Design generation (AI image models)
               Product descriptions (LLM text)
               Inventory management
               Multi-platform listing


Revenue model: Direct product sales
               Print-on-demand margins (15-30%)
               Subscription for automation tools

The workflow:

ecommerce_agent.py
class EcommerceAgent:
    def __init__(self, niche: str, platforms: list[str]):
        self.niche = niche
        self.platforms = platforms  # ["etsy", "shopify", "amazon"]


    async def generate_designs(self, count: int) -> list[str]:
        """
        Use image generation models to create
        niche-specific designs.
        """
        # Analyze trending keywords
        # Generate design variations
        # Filter by quality/appeal
        ...


    async def create_listings(self, designs: list[str]) -> list[dict]:
        """
        Generate product listings for multiple platforms.
        """
        listings = []
        for design in designs:
            listing = {
                "title": await self.generate_title(design),
                "description": await self.generate_description(design),
                "tags": await self.generate_tags(design),
                "price": await self.calculate_price(design),
            }
            listings.append(listing)
        return listings


    async def sync_inventory(self) -> None:
        """
        Manage stock across platforms.
        """
        # Sync availability
        # Update pricing
        # Track sales
        ...

The beauty of e-commerce: customers don’t care about your tech stack. They care about the product.

Market 3: Specialized B2B Services

Every business has tedious processes. Document processing, compliance checking, data entry, report generation. They pay for efficiency.

Why this works:

B2B Service Automation
Customer:      Businesses with manual processes
Payment proof: Document processing: $5-50 per document
               Compliance checking: $200-2000 per audit
               Data entry: $10-25 per hour


Agent tasks:   Invoice processing
               Contract analysis
               Compliance verification
               Report generation


Revenue model: SaaS subscription ($100-500/month)
               Per-document pricing
               Enterprise licensing

Example use case:

b2b_automation_agent.py
class ComplianceAgent:
    def __init__(self, regulation_type: str):
        self.regulation_type = regulation_type
        self.rules = self.load_regulations()


    async def audit_document(self, doc_path: str) -> dict:
        """
        Check document against compliance rules.
        """
        doc = await self.parse_document(doc_path)
        issues = []


        for rule in self.rules:
            violations = await self.check_rule(doc, rule)
            issues.extend(violations)


        return {
            "compliant": len(issues) == 0,
            "issues": issues,
            "recommendations": await self.generate_fixes(issues),
        }


    async def generate_report(self, audit_results: list[dict]) -> str:
        """
        Create compliance report for stakeholders.
        """
        # Summarize findings
        # Highlight risks
        # Provide remediation steps
        ...

Business users pay for reliability and accuracy, not technical elegance.

What Worked vs. What Didn’t

The Reddit experiment provided clear data:

Results Comparison
Approach                    Downloads    Revenue    Reviews
----------------------------------------------------------------
Dev tools for AI builders      22         $0        5 stars
(free starter kit)


What would have worked:
----------------------------------------------------------------
Local lead generation            ?        $500+      ?
(even 10 businesses at $50/mo)


E-commerce products              ?        $100+      ?
(even 20 sticker sales at $5)


B2B document processing          ?        $200+      ?
(even 5 documents at $40)

The free dev tool got attention but no revenue. Markets with existing payment behavior would have generated actual money.

The Framework: How to Choose

I created a decision framework for pointing AI agents at revenue-generating opportunities:

Market Selection Framework
Step 1: Identify existing payment behavior
        - Do customers already pay for similar solutions?
        - Is there a proven willingness to pay?
        - Are there competitors making money?


Step 2: Assess competition level
        - Technical competition? (high)
        - Non-technical competition? (lower)
        - Local/regional competition? (varies)


Step 3: Evaluate agent capability fit
        - Can agents automate the core task?
        - Is the task repetitive and rule-based?
        - Does it benefit from 24/7 availability?


Step 4: Calculate unit economics
        - Cost per task (LLM + infrastructure)
        - Revenue per task/sale/subscription
        - Break-even point


Step 5: Validate before building
        - Pre-sell to 5 potential customers
        - Confirm pricing acceptance
        - Test distribution channels
market_selector.py
from dataclasses import dataclass


@dataclass
class Market:
    name: str
    payment_behavior: bool      # Do customers already pay?
    competition_level: float    # 0-1, higher = more competition
    agent_fit: float            # 0-1, how well can agents help
    avg_revenue_per_sale: float
    cost_per_task: float


def evaluate_market(market: Market) -> tuple[bool, float]:
    """
    Returns (should_pursue, expected_roi)
    """
    if not market.payment_behavior:
        return False, 0.0


    # Lower competition + higher agent fit = better
    opportunity_score = (
        market.agent_fit * (1 - market.competition_level)
    )


    # Simple ROI estimate
    roi = (
        market.avg_revenue_per_sale / market.cost_per_task
        if market.cost_per_task > 0 else 0
    )


    should_pursue = opportunity_score > 0.3 and roi > 3


    return should_pursue, roi


# Example markets
markets = [
    Market("AI dev tools", False, 0.9, 0.9, 0, 0.05),
    Market("Local lead gen", True, 0.4, 0.7, 50, 0.10),
    Market("E-commerce", True, 0.6, 0.8, 15, 0.02),
    Market("B2B compliance", True, 0.3, 0.6, 200, 0.30),
]


for market in markets:
    pursue, roi = evaluate_market(market)
    print(f"{market.name}: pursue={pursue}, roi={roi:.1f}x")


# Output:
# AI dev tools: pursue=False, roi=0.0x
# Local lead gen: pursue=True, roi=500.0x
# E-commerce: pursue=True, roi=750.0x
# B2B compliance: pursue=True, roi=666.7x

Common Mistakes to Avoid

From analyzing the Reddit experiment and similar attempts:

Mistake 1: Building for AI enthusiasts

This market expects free tools. They compare everything to open-source alternatives. They’re the wrong customers for monetization.

Mistake 2: Ignoring distribution

A great product with no distribution channel fails. The Reddit experiment had 22 downloads but zero sales channel optimization.

Mistake 3: Over-engineering

Simple solutions to expensive problems win. You don’t need complex AI when a straightforward automation works.

Mistake 4: Chasing trends

AI agent tools are trendy but oversaturated. Boring problems in non-sexy industries have less competition.

Mistake 5: Measuring wrong metrics

Downloads and stars don’t pay bills. Revenue does. The experiment celebrated 22 downloads when it should have focused on 1 sale.

The Profitable Path Forward

AI agents should build products for markets where money already flows, not for the AI ecosystem itself. The Reddit experiment proved that even well-executed free products in the AI space generate zero revenue.

The winning strategy:

  1. Identify markets with existing payment behavior
  2. Point agents at boring, expensive problems
  3. Target non-technical customers
  4. Validate before building
  5. Focus on revenue, not downloads

The question I leave you with: What market with existing payment behavior would you point your AI agents at?

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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