Continuous Qualification with Pydantic Evals

Overview

In Life Sciences software systems, introducing changes to AI agents or retrieval logic requires continuous qualification. The platform implements an automated continuous evaluation subsystem built on Pydantic Evals (src/evals/).

This framework enforces mathematical accuracy, citation grounding, regulatory tone standards, and OpenTelemetry trace contracts across pull requests and nightly builds.

Specialized GxP Evaluator Suite (src/evals/evaluators.py)

Evaluator Class	Target Regulatory Standard	Assertion Logic
RegulatoryToneEvaluator	FDA/EMA Style Guide	Programmatically fails any response containing emoticons, emojis, or informal decorative symbols.
FmeaArithmeticEvaluator	GAMP 5 & ISO 14971	Formally asserts the mathematical equality $RPN = \text{Severity} \times \text{Occurrence} \times \text{Detection}$.
CitationFidelityEvaluator	ALCOA+ Principles	Confirms that every cited evidence code (e.g. URS-EBR-001#C2) is authentically present in the retrieved pre-flight context.
FiveWhysProgressionEvaluator	21 CFR 211.192	Verifies that root cause deduction follows a structured, multi-level causal descent with minimum required causal depth (>= 4).
CsaTestingRigorEvaluator	FDA CSA Guidance	Asserts that software testing rigor assignments correlate directly with patient safety impact and software complexity.
SlaLatencyEvaluator	System SLA Contract	Asserts that agent execution completes within bounded time limits (e.g. sub-second or < 3.0s).
HybridRetrievalSpanEvaluator	OpenTelemetry Contract	Inspects OpenTelemetry span traces (SpanTree, HasMatchingSpan) to verify that required retrieval operations were executed.

Golden Benchmark Datasets (src/evals/datasets.py)

The evaluation runner tests agents against standardized test suites:

• DEVIATION_GOLDEN_DATASET: 10 complex pharmaceutical manufacturing deviation scenarios with known ground truth root causes.
• CSA_GOLDEN_DATASET: Regulated computerized system features requiring CSA critical thinking assessment and testing rigor assignment.
• TRACEABILITY_GOLDEN_DATASET: Complex multi-tier requirements specifications with known orphaned and unverified items.

Command Line Execution

# Execute deviation benchmark suite
python -m src.evals.cli --suite deviation --enforce-gate

# Execute CSA risk benchmark suite
python -m src.evals.cli --suite csa --enforce-gate

# Execute traceability benchmark suite
python -m src.evals.cli --suite traceability --enforce-gate

# Execute all suites with minimum aggregate score threshold
python -m src.evals.cli --suite all --min-score 0.85 --enforce-gate

Pull Request CI/CD Gate

In the continuous integration pipeline, assert_evaluation_gate() enforces that any pull request that reduces evaluation benchmark scores or fails deterministic regulatory evaluators is blocked from merging.