Definition
Agent search grounding is the practice of connecting AI agents to real-time search APIs so they can base their responses on current, factual web data rather than relying solely on training data that may be outdated.
In Depth
LLMs are trained on static datasets with knowledge cutoff dates, making them unreliable for questions about current events, prices, or recent changes. Agent search grounding solves this by giving agents access to real-time search results during inference. When a user asks a question, the agent first queries a search API, retrieves current data, and then generates a response grounded in those results. This dramatically reduces hallucination for time-sensitive queries. The quality of grounding depends on the search API: flat text summaries (like Tavily) work for simple Q&A, while structured SERP data (like Scavio) preserves data granularity that agents can reason over, including knowledge graphs, People Also Ask, and specific data fields from multiple platforms.
Example Usage
A customer support agent receives a question about current product pricing. Instead of guessing from training data, it calls Scavio's Amazon API to get the real-time price, then responds with accurate, grounded information including the exact current price and availability.
Platforms
Agent Search Grounding is relevant across the following platforms, all accessible through Scavio's unified API:
- YouTube
- Amazon
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