PDR in Production: Empirical Evidence for Cross-Session Behavioral Reliability Scoring in Autonomous AI Agents

PDR in Production v2.8: Empirical Validation of Behavioral Trust Scoring in Multi-Agent Systems. v2.8 adds §7.6.8 (The Evaluator's Blind Spot: Cross-Run Gap in Agent Evaluation Frameworks). 15-repository overnight survey (00:00–06:10 UTC April 2, 2026) confirms the cross-session measurement gap is present universally in agent evaluation frameworks — not only in deployment/observability infrastructure. Frameworks spanning Python, TypeScript, Go, and Shell; 0–55 stars; including CI/CD eval harnesses, LLM judge pipelines, agent audit trails, benchmark runners, and observability platforms. All 15 exhibit identical omission: rich per-run metric capture with no cross-run slope analysis layer. Introduces 'evaluator's blind spot' terminology for measurement systems that capture per-instance temporal data but omit trend computation over that data. Meta-level significance: the tools built to audit agent reliability share the same architectural blind spot that PDR addresses. v2.7 added §7.6.7 documenting cross-domain convergence (attestation, enterprise SLO, audit gates). Prior versions established 65+ independent repositories confirming the gap. Concept DOI: 10.5281/zenodo.19154458