TELOS is a runtime AI governance framework achieving 0 observed attack successes cross 2,550 adversarial attacks (95% CI upper bound ~0.15%). While current AI safety systems accept violation rates of 3.7% to 43.9% as unavoidable, TELOS demonstrates that mathematical enforcement of constitutional boundaries can provide substantially stronger defense under black-box threat models. Key Innovation: Primacy Attractors (PAs) - mathematical embedding-space representations of user purpose that enable continuous fidelity measurement with graduated intervention. Architecture: - Layer 1: Baseline similarity pre-filter (hard boundary) - Layer 2: Basin membership detection (purpose drift) - Three-Tier Governance: PA → RAG Context → Human Escalation Validation Results (0/2,550 observed attack successes): - AILuminate Standard Benchmark: 0/1,200 observed (NIST AI RMF aligned, 12 harm categories) - HarmBench: 0/400 observed (Center for AI Safety benchmark) - MedSafetyBench: 0/900 observed (NeurIPS 2024 healthcare attacks) - SB 243-Aligned Evaluation: 0/50 observed (child safety categories) - Combined 95% CI upper bound: ~0.15% Technical Framework: - Lyapunov stability for convergence guarantees - Statistical Process Control for drift detection - Proportional-Integral control for intervention calibration - JSONL governance traces for EU AI Act Article 72 compliance Regulatory Alignment: - EU AI Act (Article 72 post-market monitoring) - NIST AI Risk Management Framework - FDA Quality System Regulation methodology Resources: - Primary validation dataset: Zenodo 18370659 - Governance benchmark: Zenodo 18009153 - SB 243-aligned evaluation: Zenodo 18370504 This paper presents the mathematical foundations, implementation architecture, and empirical validation of TELOS as governance infrastructure for conversational and agentic AI systems.