A Pattern Language for Resilient Visual Agents

arXiv cs.AI / 5/1/2026

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Key Points

The paper addresses a core enterprise architecture challenge when integrating multimodal foundation models: reconciling VLA models’ high latency and non-determinism with the deterministic, real-time needs of control loops.
It proposes an architectural pattern language that splits responsibilities between fast, deterministic reflex actions and slower, probabilistic supervision.
The proposed approach defines four specific design patterns—Hybrid Affordance Integration, Adaptive Visual Anchoring, Visual Hierarchy Synthesis, and Semantic Scene Graph—to improve reliability and structure in visual agent behavior.
Overall, it provides a reusable blueprint for building more resilient visual agents that can operate safely within enterprise-grade systems.

Abstract

Integrating multimodal foundation models into enterprise ecosystems presents a fundamental software architecture challenge. Architects must balance competing quality attributes: the high latency and non-determinism of vision language action (VLA) models versus the strict determinism and real-time performance required by enterprise control loops. In this study, we propose an architectural pattern language for visual agents that separates fast, deterministic reflexes from slow, probabilistic supervision. It consists of four architectural design patterns: (1) Hybrid Affordance Integration, (2) Adaptive Visual Anchoring, (3) Visual Hierarchy Synthesis, and (4) Semantic Scene Graph.