The Hallucination Problem
57% of organizations have AI agents in production. 32% cite quality as their top barrier. The root cause? Hallucination — AI confidently generating false information.
An LLM with a 2024 training cutoff will hallucinate about 2026 events. It doesn't know the latest Kubernetes version, current API pricing, or today's news. It guesses. Confidently.
Search Grounding: The Fix
Search grounding means feeding real-time web search results to your agent before it generates a response. Instead of relying on training data, the agent gets fresh facts.
Without grounding: > User: "What's the latest Go version?" > Agent: "Go 1.21" (hallucinated — actual is 1.25)
With SearchX grounding: > Agent searches: "latest Go version" → gets go.dev result > Agent: "Go 1.25.6, released March 2026" (correct, with source)
Implementation
# 1. Agent receives user query query = "What's the latest Go version?"
# 2. Search for grounding results = searchx.search(query, mode="hybrid", agent="coder")
# 3. Build context with citations context = "\n".join([ f"[{r.trust_score}] {r.title}: {r.snippet} (source: {r.citation})" for r in results ])
# 4. Generate grounded response response = llm.generate( f"Based on these search results:\n{context}\n\nAnswer: {query}" ) ```
Why SearchX > Raw Web Scraping
| | Raw scraping | SearchX API | |--|-------------|-------------| | Speed | 2-5 seconds | 5ms | | Data | HTML noise, ads, scripts | Clean JSON | | Trust | Unknown source quality | Trust score per result | | Citation | Manual extraction | Ready-made markdown | | Cost | Proxy + parsing infra | 1,000 free/day |
Results
Teams using SearchX for grounding report: - 90% reduction in factual hallucinations - 5x faster agent responses (vs web scraping) - Zero infrastructure overhead