This topic covers the intersection of agentic AI platforms and the infrastructure needed to underwrite, originate and manage tokenised real‑world assets (RWAs) for institutional lending. As institutional tokenisation matures, teams are combining token‑issuance and settlement rails (e.g., Plume, DigiFT) with RWA lending protocols (e.g., Morpho) and deploying AI to automate credit assessment, monitoring, reconciliation and regulatory controls. Key components of this stack include agent frameworks and marketplaces for building LLM‑powered workflows (LangChain, AutoGPT, Adept’s ACT‑1), enterprise agentic infrastructure for visibility and orchestration (Xilos), foundation models and production platforms with privacy and governance features (Mistral, Claude family, Google Gemini), plus developer tooling for on‑chain wallets and custody integration. AI data platforms and marketplaces provide the provenance, feature engineering and verified datasets needed for credit models; governance tooling ensures observability, explainability and policy enforcement across agents. Relevance and timing (mid‑2026): institutional adoption of tokenised assets has increased demand for automated, auditable pipelines that bridge on‑chain transactions and off‑chain compliance. At the same time, regulators and counterparties expect model governance, data lineage and operational controls; that pushes teams toward stacks that combine agent frameworks, enterprise‑grade models, and governance/observability layers. Practically, organisations evaluate combinations of agent SDKs, model providers, data platforms and governance tools to balance automation, explainability and custody requirements. Successful deployments will prioritise verifiable data, agent observability, and clear custody/settlement separation to reduce operational and regulatory risk.