A Pattern Language for Resilient Visual Agents
Habtom Kahsay Gidey, Alexander Lenz, Alois Knoll
TLDR
This paper introduces an architectural pattern language for resilient visual agents, balancing VLA models with enterprise control loops.
Key contributions
- Hybrid Affordance Integration
- Adaptive Visual Anchoring
- Visual Hierarchy Synthesis
- Semantic Scene Graph
Why it matters
Integrating multimodal foundation models into enterprise systems is challenging due to VLA model latency and non-determinism. This work provides architectural patterns to manage these issues, enabling robust visual agents. It helps balance real-time enterprise needs with probabilistic AI.
Original 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.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.