This topic covers platforms (for example NUVA and similar tokenization services) that integrate AI to automate issuance, management, valuation and compliance workflows for tokenized real-world assets (RWAs). These systems sit at the intersection of decentralized AI infrastructure, AI tool marketplaces, and regulatory compliance tooling: they use on-chain tokens and smart contracts for ownership and settlement while leveraging AI agents and models to ingest documents, run valuations, manage reconciliation and monitor regulatory risk. Relevance in 2026 stems from growing institutional interest in RWAs, tighter regulatory scrutiny, and maturing AI–blockchain integration patterns. Practical deployment requires developer frameworks, observability and governance: LangChain and GPTConsole provide SDKs and lifecycle tools for building and deploying LLM-powered agents that orchestrate data pipelines, oracles and contract interactions; Kore.ai supports multi-agent, governed workflows for enterprise orchestration; Google Gemini supplies multimodal APIs useful for document understanding and valuation models. Developer productivity tools such as GitHub Copilot and Amazon CodeWhisperer (now folding into Amazon Q Developer) accelerate secure smart‑contract and integration code, reducing time-to-production. Key operational considerations include oracle integrity, data provenance, model governance and explainability, KYC/AML automation, auditable trails for regulators, and composability across AI marketplaces and decentralized execution environments. Emerging best practices pair on-chain primitives with off-chain, verifiable AI workflows and observability layers so asset issuers, custodians and regulators can trace decisions. The result is a pragmatic landscape where tokenization platforms leverage AI to scale RWA workflows while balancing technical, legal and trust requirements.