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GAIF-4 extends the Governed AI Architecture Framework (GAIF) with four computable safety metrics for multi-agent clinical AI pipelines. Each metric addresses a distinct deployment-level failure mode invisible to standard model benchmarks: spontaneous content emergence (EMR), external contamination propagation (T1PR), protected health information leakage (CFR), and governance-change velocity mismatch (GDR). Unlike DORA metrics, which measure software delivery performance retrospectively from production data, GAIF-4 is a prospective risk assessment that measures pipeline hazards before they manifest in patient-facing systems. Based on a systematic scan of 65+ AI safety and governance frameworks, no existing framework provides a comparable set of computable, deployment-level, multi-dimensional safety metrics for clinical AI pipelines. Each metric is independently validated through separate research totaling over 300,000 API calls across multiple model families: EMR: Validated in the EMG paper (97K API calls, 4 model families, 3 topologies, 10 clinical domains) T1PR: Validated in the ContamPerc paper (210K API calls, 5 model families, 3 topologies) CFR: Validated in the PHI-GUARD paper (30K MIMIC-IV queries, zero violations, distribution-free bound) GDR: Validated in the GDR paper (65 events across 3 vendors, 12-month analysis) The specification defines normalization formulas, composite scoring, safety grades (A through F), threshold justifications, and deployment remediation guidance. All four FAIL boundaries map to 0.50 on the normalized scale, making the composite score directly interpretable. An open-source assessment toolkit with 59 passing tests is available at: https://github.com/aman210122/gaif-governance-observatory This is a companion to the GAIF v1.0 specification (DOI: 10.5281/zenodo.19341015). Contact: Aman_sharma007@yahoo.com ORCID: 0009-0005-5107-4485 LinkedIn: linkedin.com/in/amansharmaarchitect