Coherence as a Design Primitive in Artificial Intelligence Systems: Improving Reliability Through Constraint Alignment, Externalized Memory, and Recursive Structure

Abstract Despite rapid progress in large-scale artificial intelligence (AI) models, deployed systems frequently exhibit brittle behavior, task failure, and semantic inconsistency. These failures are often attributed to insufficient model scale or capability; however, evidence increasingly suggests that breakdowns arise from incoherent task framing, fragmented memory representation, and misaligned constraints. This paper presents a systems-level framework for improving AI reliability by treating coherence, constraint alignment, and recursive structure as primary design primitives. Drawing on our prior research into recursive symbolic systems and externalized memory architectures, we argue that intelligence performance improves when models operate within stable, bounded interaction environments. We outline practical architectural and interaction-level recommendations that can be adopted without increasing model size or autonomy, reframing AI reliability as an emergent property of structured context rather than raw computational power. Keywords: artificial intelligence, reliability, coherence, constraints, memory architecture, recursive systems