Abstract
Long-running AI agents suffer from coherence degradation as context accumulates. This paper describes an architecture in which the context window is treated not as a container that fills over time but as an assembled result — combining a bounded sliding window of recent conversation with retrieved memories, autonomous agency subsystems, and self-monitoring mechanisms. A single-agent deployment using this architecture has operated continuously for 1100+ turns across 90+ days with two hallucination events (0.18%), both attributable to infrastructure bugs rather than model drift. The system incorporates a rolling context window with periodic memory integration, a multi-factor weighted retrieval system with session-scoped warmth boosting, natural language memory tagging with epistemic attribution, dual-timescale self-monitoring, and multiple autonomous agency subsystems including persistent working memory, forward-looking intentions, and periodic reflective pulses. These results suggest that coherence degradation in long-running agents is primarily an architecture problem, not an inherent limitation of large language models.