Configure Pre-Deployment Groundedness Evaluations
Implementation Effort: Medium – Requires configuring evaluation pipelines in Azure AI Foundry, defining evaluation datasets, selecting metrics, and establishing pass/fail thresholds for agent quality gates.
User Impact: Low – Evaluations run against agent endpoints in testing environments; end users are not affected.
Overview
Azure AI Foundry provides an evaluation framework that measures AI model and agent output quality across multiple dimensions, including groundedness (whether responses are supported by provided source data), relevance (whether responses address the user's question), coherence (whether responses are logically structured), and fluency (whether responses are well-written). Configuring these evaluations establishes a pre-deployment quality gate that verifies agents produce outputs meeting the organization's standards before they reach production users.
This evaluation step is distinct from runtime groundedness detection. Runtime groundedness detection (via the Azure AI Content Safety API) operates in production, evaluating and optionally correcting individual responses as they are generated. Pre-deployment evaluations operate in the development and testing lifecycle, running the agent against a curated dataset of test inputs and systematically measuring output quality across hundreds or thousands of test cases. The purpose is different — runtime detection is a safety net that catches individual failures in production, while pre-deployment evaluation is a quality gate that measures whether the agent's overall output quality meets the bar for production deployment.
The evaluation framework uses both built-in metrics and AI-assisted metrics. Built-in metrics provide deterministic measurements such as exact match, F1 score, and BLEU score. AI-assisted metrics use a language model as a judge to evaluate subjective quality dimensions like groundedness and relevance, producing scores that approximate human evaluation at scale. Organizations should configure evaluation pipelines that run automatically when agents are updated, producing quality scorecards that development teams and security reviewers can use to decide whether an agent is ready for production.
This task supports Verify Explicitly by requiring evidence-based quality assessment before production deployment, rather than relying on developer judgment about output quality. It supports Assume Breach in the data quality context — if an agent's grounding data is poisoned or corrupted, evaluation against a known-good test dataset can detect the degradation because the agent's outputs will diverge from expected answers. Organizations that do not configure quality evaluations deploy agents without systematic measurement of output quality, discovering problems when end users report incorrect or nonsensical responses rather than during controlled testing.