Resilient Conversational AI: A Framework for Connectivity-Adaptive Inference with Graceful Degradation

Modern conversational AI systems exhibit a critical reliability gap: while model capabilities continue to advance, most production deployments remain architecturally brittle under network variability. This brittleness stems from exclusive dependence on cloud-based inference, which treats connectivity loss as a terminal failure condition rather than a degradation scenario. We introduce connectivity-adaptive inference (CAI), a systematic framework that enables conversational AI to maintain functional continuity across varying network conditions through dynamic inference mode selection. Our contributions include: (1) formalization of the inference continuity problem with quantitative resilience metrics, (2) a novel adaptive routing algorithm that balances capability and availability under network constraints, (3) a multi-tier inference architecture supporting graceful degradation, and (4) an evaluation framework for measuring conversational resilience. Through analytical modeling and simulated connectivity scenarios, we demonstrate that conversational systems can preserve 85-95% availability under intermittent network conditions while maintaining acceptable response quality. This work provides both the conceptual foundation and practical architecture for building production-ready conversational AI systems that remain usable under real-world network conditions.