This topic examines the tools and architectures used to deploy AI agents, autonomous logistics and industrial automation in production environments. It covers multi‑agent orchestration (Fujitsu-style agent frameworks), physics-aware simulation and Siemens digital twins for design, testing and predictive maintenance, and Red Hat/AWS hybrid-cloud/edge stacks that connect operational technology (OT) to cloud services and data platforms. Relevance as of 2025-12-18: enterprises are moving beyond pilots to scale AI in logistics and factories amid persistent supply‑chain disruption, labor shortages and tighter margins. That shift prioritizes safe, auditable agent behavior, stateful orchestration, simulation-first validation and hybrid deployments that keep latency‑sensitive workloads at the edge while centralizing model training and observability in the cloud. Key tools and roles: Gatik provides an Autonomous Transportation as a Service (ATaaS) for middle‑mile trucking on repeatable routes; IBM watsonx Assistant supports enterprise virtual agents and multi‑agent automations using LLMs; LangChain (and LangGraph) offers engineering frameworks for building, testing and deploying stateful agent applications; AutoGPT enables rapid assembly of autonomous agent workflows (self‑hosted or cloud); Cimba.AI delivers no‑code, auditable agents for domain experts. Complementing these are Siemens’ digital‑twin capabilities for simulation and system‑level validation and Red Hat/AWS stacks that support containerized, secure edge/cloud operations. Practical considerations: choose tools by governance needs (auditability), operational constraints (latency, connectivity), and validation path (digital‑twin testing, staged rollouts). The emerging pattern is modular stacks—agent frameworks + simulation + hybrid cloud infrastructure—that balance autonomy, safety and enterprise governance for supply‑chain and industrial automation.