Scavio for Optimize AI Agent Data Access Scope

The Problem

AI agents with broad search access waste tokens on irrelevant results and burn through API credits on queries outside their domain. A customer support agent does not need Amazon product search. A research agent does not need TikTok.

How Scavio Helps

• Scope each agent to only the platforms it needs via query routing
• Reduce token consumption by filtering irrelevant search results before they reach the agent
• Set per-agent daily credit budgets to prevent runaway costs
• Single API key with application-level access controls
• Audit log per agent showing query volume, platforms used, and cost

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "configure agent search scope per platform":

import requests

API_KEY = "your_scavio_api_key"

response = requests.post(
    "https://api.scavio.dev/api/v1/search",
    headers={
        "x-api-key": API_KEY,
        "Content-Type": "application/json",
    },
    json={"query": query},
)

data = response.json()
for result in data.get("organic_results", [])[:5]:
    print(f"{result['position']}. {result['title']}")
    print(f"   {result['link']}\n")

Built for Platform engineering teams managing multi-agent architectures with search tool access

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your optimize ai agent data access scope solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (50 credits on signup, no credit card required) and scale to paid plans when you need higher volume.