Graceful Degradation in Autonomous Agents: SAGE Memory-Augmented Drone Navigation Without Language Model Dependency V2

Autonomous drones that rely on language models for decision-making fail completely when connectivity is lost. This paper presents a proof-of-concept study demonstrating graceful degradation: a drone agent that continues operating at full decision rate when its LLM is disabled, using SAGE (Spatial Associative Geometric Embeddings) geometric memory as a fallback. Across all test modes (72 total agent steps), zero default decisions were recorded — the agent never stopped. SAGE-only geometric retrieval averages 2.09 seconds versus 7.49 seconds for LLM inference — a 3.6x speedup. An architectural ablation shows SAGEDivided working memory improves SAGE-only meaningful recall by 60% (5/12 to 8/12). SAGESequenceCube, a dedicated transition memory component, achieves 100% retrieval and rollout accuracy, thereby resolving the sequence-awareness gap. All experiments use text-command simulation — sensor integration is identified as future work.