Building for the Agent Economy: How Startups Should Think About AI Infrastructure

The Coming Agent Economy

Today's AI landscape:

• Humans use AI tools directly
• Humans make all purchasing decisions
• Humans evaluate vendors and integrations

Tomorrow's AI landscape:

• Agents use other agents as tools
• Agents have budgets and spending authority
• Agents evaluate services programmatically

This isn't science fiction. We're seeing early signs now: agents calling APIs, agents choosing between services, agents optimizing for cost and quality.

What Agent-Compatible Means

Clear, Consistent APIs

Agents need predictable interfaces:

// Agent-friendly: Consistent, typed, documented
POST /api/v1/analyze
{
  "content": "...",
  "analysis_type": "sentiment",
  "options": { "language": "en", "detailed": true }
}
Response: { "result": {...}, "confidence": 0.92, "tokens_used": 150 }

// Agent-hostile: Inconsistent, magic strings, surprises
POST /analyze?type=sentiment&lang=en&v=2
Response: Sometimes JSON, sometimes HTML, error codes vary

Programmatic Pricing

Agents need to understand costs:

GET /api/v1/pricing
{
  "operations": {
    "analyze": { "cost_per_call": 0.001, "cost_per_token": 0.00001 },
    "generate": { "cost_per_call": 0.01, "cost_per_token": 0.0001 }
  },
  "volume_discounts": [...],
  "rate_limits": { "per_minute": 100, "per_day": 10000 }
}

Machine-Readable Capabilities

Agents need to know what you can do:

GET /api/v1/capabilities
{
  "services": [
    {
      "name": "sentiment_analysis",
      "inputs": ["text"],
      "outputs": ["sentiment_score", "confidence", "keywords"],
      "limitations": { "max_length": 10000, "languages": ["en", "es", "fr"] }
    }
  ]
}

Infrastructure That Wins

Persistent Context

Agents need memory across interactions:

• Store conversation history
• Remember preferences and patterns
• Maintain state between sessions

Reliable APIs

Agents can't handle ambiguity:

• Consistent error handling
• Clear rate limiting behavior
• Predictable response times

Clear Interfaces

Agents need to understand your service:

• OpenAPI/Swagger specifications
• Example requests and responses
• Machine-readable documentation

Revenue Models in the Agent Economy

Per-Agent Pricing

Charge based on agent seats:

pricing: {
  "agent_seat": "$49/month",
  "includes": "100k API calls",
  "overage": "$0.001 per call"
}

Context Metering

Charge based on context usage:

pricing: {
  "context_storage": "$0.01 per MB/month",
  "context_retrieval": "$0.001 per query",
  "context_updates": "$0.0001 per write"
}

Outcome-Based

Charge based on results:

pricing: {
  "successful_analysis": "$0.10",
  "failed_analysis": "free",
  "quality_threshold": "confidence > 0.8"
}

Strategic Positioning

Be the Platform

The most valuable position: agents want to connect to you.

• Rich context layer that improves agent performance
• Network effects from agent interactions
• Data that agents need but can't get elsewhere

Be the Tool

Specialized capability that agents need:

• Specific, well-defined functionality
• Best-in-class at one thing
• Easy to integrate, hard to replicate

Be the Orchestrator

Coordinate multiple agents and tools:

• Workflow management for agents
• Quality control across agent outputs
• Cost optimization across services

What to Build Now

1. API-first architecture. If agents can't call it, agents can't use it.
2. Machine-readable docs. OpenAPI specs, not just markdown.
3. Programmatic pricing. Agents need to calculate costs.
4. Usage analytics. Understand how agents use your service.
5. Context layer. Let agents store and retrieve state.

Signs You're Falling Behind

• Your API requires human signup flows
• Pricing is only available via "contact sales"
• Documentation assumes human readers
• No programmatic way to discover capabilities
• Error messages require human interpretation

The agent economy isn't coming in 10 years. The first wave is here. Position now or catch up later.