Industrial digital twins and AI-driven supply‑chain resilience combine simulation, live operational data, agentic automation, and autonomous logistics to reduce disruption risk and meet decarbonization targets. As of late 2025, organizations are deploying stateful agent frameworks, no‑code orchestration, and specialized data pipelines to run real‑time twins that integrate IoT telemetry, LLM reasoning, optimization models, and verified emissions data. Key categories and tools: AI Automation Platforms (IBM watsonx Assistant, MindStudio, Cimba.AI) provide enterprise-grade agent orchestration, no‑code/low‑code design, and audited multi‑agent workflows for operational playbooks and exception handling. AI Data Platforms and engineering frameworks (LangChain) enable building, testing, and deploying reliable agentic applications and stateful graphs that tie models to time‑series, simulation states, and business logic. Autonomous Logistics Tools (Gatik) deliver middle‑mile Autonomous Transportation as a Service (ATaaS), integrating deterministic route automation with fleet telemetry that digital twins consume. Sustainability/Scope 3 tooling (Mavarick AI) automates supplier data collection, validation, and carbon reporting so twin simulations reflect realistic emissions and compliance constraints. Practical impact: combining these layers lets teams run what‑if scenarios, route optimization, emissions-aware sourcing, and automated incident response inside living digital twins. Trends influencing adoption include increased expectation for auditable agent behavior, demand for no‑code governance to scale domain experts’ workflows, and tighter coupling of logistics telemetry with carbon accounting. Selecting platforms depends on priorities—autonomy and hardware integration for logistics, robust data lineage and emissions modeling for sustainability, and flexible agent engineering for decision automation—while ensuring enterprise controls, traceability, and real‑time interoperability.