AI-native blockchains and machine-native DeFi describe an emergent layer of infrastructure where blockchains are designed to host, verify, and economically coordinate AI agents and automated financial workflows. This topic covers decentralized AI infrastructure—on‑chain or hybrid compute, model and data provenance, verifiable execution (zk proofs, TEEs), and market mechanisms that let agents transact, stake, and borrow—plus post‑quantum blockchain tools needed as cryptographic threats evolve. Developer toolchains accelerate this transition: LangChain and LlamaIndex provide standard SDKs and orchestration primitives for building LLM-powered agents and retrieval‑augmented workflows that can be adapted to interact with ledgers and oracle networks. Code-focused models and assistants—GitHub Copilot, CodeGeeX, Salesforce CodeT5, Amazon CodeWhisperer (via Amazon Q Developer), Replit, and Stable Code—speed smart‑contract generation, auditing, testing, and deployment, while also creating new security considerations around autogenerated code and supply‑chain provenance. As of 2026‑05‑07, relevance is driven by production deployments of agentic services, demand for machine-to-machine economic activity, and accelerating attention to cryptographic resilience: post‑quantum signatures, key management, and quantum‑resistant consensus are increasingly practical design requirements. Key trends include hybrid on/off‑chain execution with verifiable attestations, model marketplaces with usage accounting, developer SDKs that bridge LLM agents to blockchain APIs, and tooling to automate secure contract creation. Challenges remain around latency, gas and compute costs, verifiability of model outputs, and governance. Understanding how developer platforms and code models integrate with decentralized AI primitives and post‑quantum cryptography is essential for building auditable, interoperable, and resilient machine‑native DeFi systems.