Enterprise agentic AI platforms are being used to compose, run and govern multi‑agent automation for supply‑chain and operational workflows. These systems combine agent orchestration, no‑code/low‑code design, data curation and human‑in‑the‑loop controls to automate multistep processes such as exception triage, yard orchestration, order orchestration and asset maintenance. Practical integrations with ERP and field systems (e.g., IFS), autonomous logistics providers (e.g., PlusAI) and visibility platforms (e.g., project44) let agents act on real‑time telemetry and trigger downstream actions. Key platform types include enterprise automation platforms and agent frameworks (Kore.ai for governed multi‑agent workflows; StackAI and MindStudio for no‑code/low‑code agent design and governance), agentic action engines that operate inside software interfaces (Adept/ACT‑1), CX/EX‑focused agent deployments and managed models (Yellow.ai, Crescendo.ai), specialist services for training data (DatologyAI), and open/self‑hosted automation runtimes for autonomous agents (AutoGPT). Each plays a role: orchestration and observability, multichannel worker automation, human+AI escalation, model training and lightweight deployment. As of 2026, organizations prioritize resilience, speed and compliance—driving demand for composable agent architectures that integrate visibility, autonomous transport and enterprise systems. Trends include marketplaces and modular agent libraries, stronger governance/observability, and tighter data‑ops to reduce model size and improve reliability. Successful implementations focus on clear handoffs, auditability and safety controls when connecting agents to physical logistics (e.g., PlusAI) or mission‑critical ERPs (e.g., IFS) via visibility layers like project44.