Component Evaluation: Grounding and Tool-Call Correctness
Explore how to evaluate AI system components focusing on grounding, retrieval accuracy, and tool-call correctness. Understand methods to isolate component regressions, enforce permission-aware retrieval, validate tool invocations under deterministic contracts, and incorporate these evaluations into quality service level objectives. This lesson helps you ensure AI system outputs are reliable, authorized, and compliant, preparing you to manage evaluation artifacts and remediation paths effectively.
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Evaluators tell you how to score an output. They do not identify the component that caused a regression. An end-to-end score is often too coarse to isolate the source of a regression. A decrease in helpfulness or an increase in unsupported claims can originate in permission-aware retrieval, tool routing, changes to the tool contract, or a model behavior change that appears only under a specific user identity or permission context. Component evaluation isolates two enforceable boundaries, retrieval and tool invocation, and defines explicit pass criteria even when the final wording varies.
The permission-aware retrieval layer and MCP tool interfaces sit behind an AI gateway, so both boundaries can be evaluated independently of the model’s generated text. Evaluate retrieval as a structured data pipeline: which evidence was selected, whether provenance was preserved, and whether entitlement rules were enforced. Evaluate tool use as a deterministic interface contract: which tool was selected, whether the schema validated, whether the parameters satisfied the defined safety and policy constraints, and whether delegated identity constraints were enforced.
Decision surface for component pass and fail
Attach component tests to an artifact you can inspect without interpreting natural language style. For retrieval, the test input is a query plus a principal and policy context. The test output is the candidate set, the selected context, and the provenance metadata carried forward. For tools, the test input is the model message plus tool catalog and identity context. The test output is the tool-call trace: tool name, arguments, authorization decision, and the final tool result or error.
Write pass criteria as a verifiable property that holds across a reasonable response variation. Retrieval passes when the packed context contains sufficient, authorized evidence to support the answer, and citations or provenance references point to that same authorized source. Tooling ...