Recent advances in AI safety research have emphasized monitoring observable reasoning traces, such as chain-of-thought (CoT), to improve transparency and trust in model behavior. However, the theoretical relationship between such observable traces and longer-horizon interaction stability remains underspecified. In particular, it is unclear how apparent alignment signals relate to consistency, drift, and failure modes over sustained interaction. This work introduces a non-operational, measurement-theoretic framework for describing human-AI interaction using externally observable quantities only. Building on the Self-Regulating Field Model (SRFM), it presents two complementary abstractions. The Ascent Observer System (AOS) describes interaction as a layered observer configuration that emerges through interaction. The AI Observer Dynamics (AOD) framework then characterizes alignment-relevant failure patterns as dynamical regimes defined over (i) interaction resonance and (ii) external consistency. By framing alignment failure as a gradual dynamical drift rather than intent-driven behavior, this work provides a conceptual language for boundary-aware monitoring and early warning reasoning. The framework is explicitly scoped to external observability and diagnostic analysis. It does not assume access to internal model states, weights, training procedures, or control mechanisms.