The evolution of agentic systems represents a significant milestone in AI and modern software systems, driven by the demand for vertical intelligence tailored to diverse industries. This work defines a level of standardization for Vertical AI agent design patterns, identifying core building blocks and proposing a Cognitive Skills Module incorporating domain-specific, purpose-built inference capabilities. It offers a comprehensive introduction to agentic systems, detailing their core components, operational patterns, and implementation strategies for enterprise deployment.

Many sectors including manufacturing, healthcare, and logistics demand AI systems capable of extending intelligence into the physical world, interacting directly with objects, environments, and dynamic conditions. This work introduces Physical AI Agents as an evolution of Vertical AI Agents tailored for physical interaction, proposing a modular architecture with three core blocks — perception, cognition, and actuation — offering a scalable framework for deploying AI in physical environments across diverse industries.

AI agents used in long multi-turn workflows degrade due to loss of constraint focus, error accumulation, and memory-induced drift. Existing transcript replay and retrieval-based memory approaches introduce unbounded context growth vulnerable to noisy recall and memory poisoning. This work introduces the Agent Cognitive Compressor (ACC), a bio-inspired memory controller that replaces transcript replay with a bounded internal state updated online at each turn, maintaining behavioral consistency and eliminating drift across extended enterprise deployments.

Building on the Agent Cognitive Compressor (ACC) framework, this forthcoming work introduces a bionic memory architecture for AI agents modeled on biological memory systems — integrating episodic, semantic, and working memory structures to enable long-horizon coherence, adaptive recall, and constraint-preserving behavior across complex multi-agent deployments.

Industrial computer vision systems struggle with noise, material variability, and uncontrolled imaging conditions. This work presents a language-guided generative vision system for remnant contour detection in manufacturing, achieving CAD-level precision across three stages: data preprocessing, contour generation via conditional GAN, and multimodal contour refinement through vision-language modeling with human-in-the-loop prompt engineering. Benchmarked against Gemini 2.0 Flash, GPT-image-1, and other leading vision-language models on proprietary FabTrack datasets.