Scavio for Agent Search Error Handling Patterns

The Problem

AI agents in production crash or produce garbage output when search API calls fail due to rate limits, timeouts, or empty responses, because most agent implementations lack robust error handling for tool calls.

How Scavio Helps

• Exponential backoff handles rate limits gracefully
• Configurable retry logic for timeout errors
• Response quality validation before passing to agent
• Fallback responses prevent agent crashes on search failure
• Production-ready patterns for any agent framework

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "AI agent search API error handling retry production patterns 2026":

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 AI engineers deploying agents to production, DevOps teams supporting agent infrastructure, and platform teams building reliable agent systems

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

Start with the free tier (250 credits/month, no credit card required) and scale to paid plans when you need higher volume.