Architecting Agentic AI Systems with Multimodal Reasoning for Scalable Visual Pattern Recognition

Abstract

Modern progress in agentic and multimodal AI, including ReAct, HuggingGPT, and MM-ReAct, show that large language models can coordinate vision tools by using planner executor loops. Nevertheless, all these frameworks are of ad hoc nature: they do not include a principled model of cost-conscious decision making, formal memory-verification, and reproducible architectures of large-scale visual reasoning. In a bid to fill these gaps, we propose Agentic Multimodal Pattern Recognition (AMPR) a formal reasoning and planning system that combines hierarchical decomposition, probabilistic self-checking and dynamic cost-conscious inference with a common optimization problem. In contrast to earlier models, AMPR is a clear graphical reasoning as a constrained optimization problem to trade-off accuracy, latency and cost, and integrates episodic and semantic memory to promote instead of a single step of reasoning. We submit theoretical background as well as empirical performance across benchmarks of classification, detection, segmentation, and visual question answering. Findings demonstrate that AMPR has better accuracy-efficiency tradeoffs, and is better behaved to distribution shifts with demonstrable reasoning consistency guarantees. AMPR defines a new standard of scalable, interpretable and resource-efficient visual intelligence by integrating formal algorithmic contributions and system-level validation.