AI-powered onchain app development workflows bring together agentic LLM tooling, code-generation models, low-code builders, and blockchain primitives to streamline creation, deployment and governance of decentralised applications. This topic covers Noah V2 and competing stacks that integrate stateful agent orchestration with smart-contract interactions, developer SDKs, and onchain model primitives for monetization and provenance. Relevance in late 2025 stems from two converging trends: mainstreaming of chain-native AI (agents that hold state across transactions and interact with contracts) and stronger enterprise/regulatory focus on model governance and privacy. Key categories include AI Code Generation Tools (Code Llama, GitHub Copilot, Tabnine) for producing and vetting smart-contract and off-chain integration code; Low-Code Workflow Platforms (Replit-style web IDEs and visual builders) for rapid prototyping and deployment; AI Tool Marketplaces and decentralised infra (Tensorplex Labs) that combine model hosting, staking and onchain incentives; and AI Security Governance and Regulatory Compliance tools that ensure auditable provenance, access controls and automated policy checks. Representative tools: LangChain (engineered frameworks and LangGraph for stateful agent orchestration), Tensorplex Labs (open, DeFi-aware AI infra), GitHub Copilot and Code Llama (developer-facing code models), Tabnine and Cline (enterprise/self-hosted and client-side coding agents), Replit (host+IDE+agents), and AutoGPT (automation of multi-step agent workflows). Practical implications: teams should evaluate stacks on state management, onchain integration patterns, private/self-hosted model support, auditability, and compliance hooks. The most practical approaches combine robust code-generation models, low-code deployment paths, marketplaces for verifiable models, and governance tooling to reduce technical and regulatory risk when building onchain AI applications.