From Micro-Failure Tags to Defensive Syndromes: A Technical Framework for the Core Six User-Facing Failure Modes in AI Assistants

This paper introduces the Core Six AI Defensive Behavior Syndromes: six behaviorally coherent, user-facing failure modes that map bidirectionally to and from the granular micro-failure tags currently used by AI evaluation practitioners. The six syndromes are Plausible Helpfulness, Built-Not-Connected, Hollow Completions, Capability Masking, Responsibility Diffusion, and Surface Compliance. The framework addresses a structural vocabulary mismatch in AI evaluation: technical teams use granular micro-failure taxonomies precise enough for debugging but opaque for organizational governance, while governance stakeholders describe the same failures in user-experience terms that are accurate but non-actionable for engineering. The Core Six serve as a meso-level translation layer — granular enough to guide remediation, comprehensible enough for governance use — without replacing existing evaluation infrastructure. Each syndrome is defined with dual-lens profiles: a phenomenological description for governance and user-experience contexts, and a technical anchor for engineering diagnosis. Each maps explicitly to a cluster of 44 micro-failure tags drawn from existing evaluation literature. The framework is grounded in the Breaking Through study: 18 months of continuous practitioner immersion across multiple commercial AI systems (Claude 3.5 Sonnet, GPT-4, GitHub Copilot), yielding 105 collected failure episodes with 45 carrying complete syndrome coding at publication. Syndrome categories were derived through a two-phase hybrid methodology combining emergent observational coding with confirmatory cross-taxonomy mapping. Category saturation was confirmed when all 44 micro-failure tags mapped to existing syndromes without requiring new categories. The Core Six are explicitly distinguished from AI Cognitive Overload Syndrome (ACOS), a separate failure family characterized by catastrophic coherence collapse rather than the chronic defensive posturing described here. Operational artifacts accompanying this framework include evaluation dashboard designs, incident report templates, model card enhancements, and procurement language. A public inter-rater reliability study is currently underway at https://yeahitsme.com/join-irr. Companion documents included in this package: Public Verification Appendix (v4) Supplementary Materials (v3) Verification Report Audit Trail (available upon request) Keywords: AI failure taxonomies, defensive behavior syndromes, micro-failure tags, hallucination, plausible helpfulness, built-not-connected, hollow completions, capability masking, responsibility diffusion, surface compliance, ACOS, AI governance, AI evaluation, cross-functional AI communication