Agentic AI

Here a search call is the agent's tool. The agent decides it needs a current fact, calls Scavio's Google endpoint, and acts on the structured JSON it gets back: ```python import requests def search_tool(query: str) -> dict: """A grounding tool the agent calls mid-loop.""" resp = requests.post( "https://api.scavio.dev/api/v1/google", headers={"Authorization": "Bearer YOUR_API_KEY"}, json={"query": query, "light_request": False}, ) resp.raise_for_status() return resp.json() # Agent loop: perceive -> decide it needs facts -> act (call tool) -> observe data = search_tool("current EV tax credit rules 2026") top = data["organic"][0] print(top["title"], top["link"]) # The agent now reasons on top["snippet"] instead of stale training data. ``` The response includes organic results, people_also_ask, knowledge_graph, and related_searches, so the agent has structured fields to branch on rather than a wall of text.

Agentic AI is relevant to Google, Reddit, YouTube. Scavio provides a unified API to access data from all of these platforms.

A common explanation in developer threads goes like this: generative AI creates things, agentic AI does things. Someone in an r/NBIS_Stock discussion put it as hospital triage. A triage nurse doesn't generate a paragraph about your symptoms. She assesses the situation and acts: routes you, orders a test, escalates. An agent is meant to do the same with software. The core mechanic is a loop, not a single response. The agent perceives its current state, decides on a next step, acts by calling a tool or API, then observes the result and repeats until the goal is met or it gives up. Perceive, decide, act, observe. A plain chat completion stops after one turn. An agent keeps the loop running. Grounding is the part chat won't volunteer. A model's weights are frozen at training time, so an agent reasoning only from its own knowledge acts on stale facts. Ask it for a current price, a today headline, or a competitor's plan and it confidently makes something up. Tools fix this. A search API is the most common grounding tool: the agent calls it mid-loop, gets current structured data back, and decides on real facts instead of memorized ones. The other piece people mention is role coordination. Instead of one model doing everything, you assign roles. One agent acts as product manager, one as developer, one as QA, and they pass work between each other without a human in the loop. This is the multi-agent pattern. It scales reasoning but multiplies the calls, so each agent still needs grounded data to avoid compounding wrong assumptions. Where does Scavio fit? It's a grounding tool, not an agent framework. Scavio is a Search API over Google, YouTube, Reddit, Amazon, Walmart, and TikTok that returns structured JSON. An agent built with LangChain, CrewAI, or an MCP client calls Scavio when it needs a fact it doesn't have. Google SERP costs 2 credits at full features (1 light), most other endpoints 1 credit, at $0.005 per credit. Be honest about the boundary: Scavio doesn't decide or plan. It answers the agent's question with live data so the agent's next decision is grounded.