Agents-as-a-Service Data Layer

Every agents-as-a-service platform needs a data layer for live web data. Agents cannot reason about current prices, recent news, or real-time market conditions without a search capability. Credit-based search APIs fit agent billing models naturally: the agent consumes credits proportional to its work, and MCP integration makes the search tool plug-and-play.

Why agents need a data layer

LLMs have training data cutoffs. An agent answering "what is the cheapest flight to Tokyo next week" needs live data. An agent researching a company needs current news. An agent comparing products needs current pricing. Without a data layer, agents hallucinate or return stale information.

• Market research agents: need current competitor data, pricing, reviews
• Content agents: need current facts, stats, and source URLs for citations
• Sales agents: need current company info, tech stack, recent news
• Customer support agents: need current product documentation, pricing, availability

Credit-based APIs match agent billing

Agent platforms bill per task or per credit. A search API that charges per query maps directly: task cost = LLM tokens + search credits + tool calls. This makes pricing predictable for both the platform and the end user. Subscription-based search APIs create a mismatch -- the platform pays a fixed monthly fee regardless of how many agent tasks use search.

import requests, os

H = {"x-api-key": os.environ["SCAVIO_API_KEY"]}

class AgentDataLayer:
    """Data layer for an agent-as-a-service platform."""

    def __init__(self, api_key: str, cost_per_query: float = 0.005):
        self.api_key = api_key
        self.cost_per_query = cost_per_query
        self.task_costs = {}

    def search(self, task_id: str, query: str, platform: str = "google"):
        resp = requests.post("https://api.scavio.dev/api/v1/search",
            headers={"x-api-key": self.api_key},
            json={"query": query, "platform": platform})
        # Track cost per task
        self.task_costs[task_id] = self.task_costs.get(task_id, 0) + self.cost_per_query
        return resp.json()

    def get_task_cost(self, task_id: str) -> float:
        return self.task_costs.get(task_id, 0)

data_layer = AgentDataLayer(os.environ["SCAVIO_API_KEY"])

# Agent task uses search as needed
data_layer.search("task_123", "CRM pricing comparison 2026")
data_layer.search("task_123", "HubSpot vs Salesforce reviews", "reddit")
print(f"Task search cost: {data_layer.get_task_cost('task_123'):.3f}")

MCP integration: plug-and-play

{
  "mcpServers": {
    "web-search": {
      "url": "https://mcp.scavio.dev/mcp",
      "headers": {
        "Authorization": "Bearer PLATFORM_API_KEY"
      }
    }
  }
}

With MCP, the agent discovers the search tool automatically. No custom HTTP code needed in the agent. The platform configures the MCP server once, and every agent on the platform can search the web. Authentication is handled at the platform level, not per-agent.

Multi-platform data for richer agent responses

• Google: general web search, local businesses, news
• Amazon: product data, pricing, reviews for e-commerce agents
• Reddit: user opinions, reviews, discussions for sentiment analysis
• YouTube: video content, tutorial discovery, channel analytics
• TikTok: social trends, creator data, hashtag performance

Economics at platform scale

A platform running 1,000 agent tasks/day, each averaging 5 searches: 5,000 searches/day x $0.005 = $25/day = $750/mo. At Scavio's Growth plan ($500/mo for 200K credits), the effective cost drops to $0.0025 per search. The platform marks up search costs as part of its per-task pricing, making data layer costs transparent and profitable.