As autonomous systems increasingly demonstrate more sophisticated reasoning capabilities and make higher-level decisions, the need for interpretable runtime guidance becomes critical. Traditional Models@Runtime serve as abstractions that reflect system state to support adaptation and decision-making by external actors. We extend this paradigm by introducing Ambient Advisory Models, where model components such as classes, agents, or behavioral specifications are augmented with embedded reasoning capabilities that observe, interpret, and advise. Unlike conventional runtime models that provide passive structural or behavioral representations, each model component in our approach becomes an active advisory entity, continuously monitoring its domain of concern and generating contextual guidance. These advisory components operate without direct actuation authority, functioning as cognitive guardrails that provide guidance on safety, regulatory, ethical, and other relevant concerns, while enabling multi-perspective reasoning. Rather than a monolithic reasoning model, we distribute advisory intelligence across individual model components, each maintaining its own reasoning context and concern-specific knowledge. We demonstrate Ambient Advisory Models in autonomous multi-UAV emergency response operations. This approach transforms selected runtime models from reflective artifacts into proactive advisors, enabling a new form of human-AI collaboration where model components actively participate in system governance rather than merely representing system state.