A Pattern Language for Resilient Visual Agents

TL;DR

This paper proposes an architectural pattern language for visual agents that balances real-time deterministic responses with probabilistic vision models in enterprise systems.

Contribution

It introduces four specific design patterns to structure visual agents for improved resilience and performance in enterprise environments.

Findings

Four architectural design patterns are proposed.

Patterns enable separation of deterministic reflexes and probabilistic supervision.

The approach improves system resilience and real-time performance.

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.