Grounding
Grounding is the practice of connecting an AI agent's reasoning to verifiable external information sources to reduce hallucination and ensure outputs are factually accurate and contextually relevant.
title: "Grounding" type: glossary tags: [#reasoning, #tool-use] created: 2025-07-14 updated: 2025-07-14 status: stub
Grounding
Grounding is the process by which an AI agent connects its reasoning and outputs to verifiable external information sources, reducing hallucination and ensuring factual accuracy.
Overview
Grounding is a critical property for agentic AI systems operating in information-rich, high-stakes environments. An ungrounded agent reasons purely from its training data, which may be outdated, incomplete, or simply wrong for a specific context. A grounded agent supplements its reasoning with real-time retrieval from authoritative sources — web searches, enterprise databases, knowledge bases, or documents — before drawing conclusions or taking actions.
In the context of Tool Use, grounding is achieved by equipping agents with retrieval tools (web search, vector database queries) that return relevant, current information. The ReAct Framework makes grounding explicit: the Observation step, which captures tool output, is what grounds the agent's subsequent Thought in real-world evidence rather than model priors.
Grounding is particularly important for data agents (which must ensure factual integrity of analytical outputs) and for any agent making consequential decisions based on domain-specific or time-sensitive information. Google Cloud's Vertex AI Agent Builder explicitly provides grounding in enterprise data as a core capability.
Related Pages
Used by: Agentic AI, Tool Use Enables: ReAct Framework See also: Memory Systems, Agent Architecture
Page type: glossary | Status: stub