Stop Your AI Agent From Hallucinating Live Data

The Problem

An AI agent answering with current prices, recent events, or live product data is guessing. Its training data is frozen at a cutoff, so anything that changed after that, today's price, this week's release, the current top result, gets confidently invented. This is exactly what the r/GoogleGeminiAI thread was about: a model that lost live access started returning plausible nonsense. For a chatbot that is annoying; for an agent taking actions on bad data, it is a real failure.

The Scavio Solution

Ground the agent: before it answers anything time-sensitive, have it call a real search API, then answer only from what came back. Retrieval-augmented generation for the open web. The pattern is simple, detect when a query needs current data, fetch it with one API call, inject the results into the prompt, and instruct the model to cite only the retrieved data. Scavio returns Google SERP, Reddit, YouTube, Amazon, Walmart, and TikTok as structured JSON through one key, so a single grounding tool covers web, social, and commerce facts.

Who It Is For

Developers building research agents, RAG pipelines, customer-facing assistants, or any agent that answers questions about prices, current events, or live product data and cannot afford to guess.

Key Benefits

• Answers reflect today, not the training cutoff
• One API key grounds web, social, and commerce queries
• light_request false returns People Also Ask and knowledge graph for richer context (2 credits)
• Credit pricing ($0.005/credit, 50 free) means grounding is cheap per call
• The model can say "not verified" instead of inventing a number

Python Example

import os, requests

H = {"Authorization": f"Bearer {os.environ['SCAVIO_API_KEY']}", "Content-Type": "application/json"}

def grounded_answer(question):
    serp = requests.post("https://api.scavio.dev/api/v1/google",
        headers=H, json={"query": question, "light_request": False}).json()
    context = serp["data"]["organic_results"][:5]
    prompt = f"""Answer using ONLY this live data. If it is not here, say you could not verify.
Data: {context}
Question: {question}"""
    return call_llm(prompt)  # your LLM call

JavaScript Example

const H = { Authorization: `Bearer ${process.env.SCAVIO_API_KEY}`, "Content-Type": "application/json" };

async function groundedAnswer(question) {
  const res = await fetch("https://api.scavio.dev/api/v1/google", {
    method: "POST", headers: H,
    body: JSON.stringify({ query: question, light_request: false }),
  });
  const { data } = await res.json();
  const context = data.organic_results.slice(0, 5);
  const prompt = `Answer using ONLY this live data. If it is not here, say you could not verify.\nData: ${JSON.stringify(context)}\nQuestion: ${question}`;
  return callLLM(prompt); // your LLM call
}

Platforms Used