This topic covers AI tools and platforms used in industrial and manufacturing settings—particularly for predictive maintenance, computer vision inspection, and intralogistics—focusing on how edge vision, agent frameworks, and data platforms work together. By 2026, deployments increasingly combine on‑device inference (for low latency and bandwidth savings) with cloud models and LLM orchestration for analytics, root‑cause analysis, and workflow automation. Key categories include Edge AI Vision Platforms (device and camera-based CV for defect detection, safety monitoring, and inventory audits), AI Automation Platforms (agent frameworks and workflow automation for scheduling, alerts, and decisioning), and AI Data Platforms (time‑series/sensor ingestion, labeling, embeddings, model training, and observability). Representative tools: Gather AI provides autonomous‑drone and MHE‑mounted camera solutions for continuous warehouse digitization; LangChain supplies an agent and orchestration framework for building, testing, and deploying LLM‑driven automation; CargoBrain demonstrates domain‑specific AI agents for freight and logistics workflows; Cohere and Google Gemini offer enterprise LLM capabilities—custom models, embeddings, and multimodal APIs—that teams use for retrieval, analytics, and natural‑language interfaces to operations data. Practical considerations include latency and bandwidth (favoring edge inference), data pipelines and labeling for reliable predictive maintenance models, model governance and privacy for on‑premises production, and integration of vision outputs into automated workflows. Emerging trends to watch are tighter integration between edge vision and LLM orchestration, greater use of domain‑specific agents, and mature data‑platform tooling for continuous model retraining and observability—each critical for reducing downtime and improving throughput in industrial environments.