Enforce Runtime Groundedness Detection
Implementation Effort: Medium – Requires integration into agent orchestration pipelines and configuration of detection behavior, plus testing to calibrate thresholds against source data.
User Impact: Medium – Agent responses may be modified or blocked when groundedness issues are detected, affecting response quality and latency.
Overview
Runtime groundedness detection evaluates whether an agent's response is factually supported by the source documents and grounding data provided to the language model, checking every response in production before it reaches the user. When an agent generates a response that includes claims not substantiated by its source material — commonly called hallucinations — the groundedness detection API flags the unsupported content. The correction capability can automatically rewrite the flagged portions to align with the source data, producing a response that is factually anchored to the provided context. This is distinct from pre-deployment groundedness evaluations in Azure AI Foundry, which test agent quality against curated datasets during development. Runtime groundedness detection is the production safety net that catches individual hallucinations on every response, in real time.
The agent's orchestration code calls the groundedness detection API with both the generated response and the source documents used for grounding, and the API returns a result indicating which portions of the response are grounded versus ungrounded. When correction is enabled, the API also returns a revised version of the response with ungrounded claims removed or rewritten. Development teams must decide how to handle detection results — whether to silently apply the correction, flag the response for human review, or block delivery entirely when groundedness falls below an acceptable threshold.
This task supports Verify Explicitly because it requires agents to validate their own outputs against source evidence before delivering them to users. Language models generate text probabilistically, and even well-configured agents produce unsupported claims when the model fills gaps in its grounding data with plausible-sounding but fabricated information. In high-stakes domains like financial advice, medical guidance, or legal interpretation, hallucinated content can cause material harm. Groundedness detection provides an automated verification layer that catches these errors at runtime without requiring manual review of every response.
The task also supports Assume Breach in the context of data poisoning. If a threat actor manages to introduce misleading content into an agent's grounding data sources, the agent may generate responses that appear authoritative but are based on compromised source material. While groundedness detection does not prevent data poisoning, it ensures that the agent's responses are traceable to specific source documents, making it easier for security teams to identify and investigate when an agent begins producing suspicious outputs that trace back to a compromised data source. Organizations that do not implement runtime groundedness detection have no automated means to catch hallucinations before they reach end users. They rely entirely on users to identify inaccurate responses, which assumes expertise that most business users lack. In regulated industries, delivering ungrounded AI responses to customers or stakeholders can create compliance and liability exposure.