> For the complete documentation index, see [llms.txt](https://azen.gitbook.io/whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://azen.gitbook.io/whitepaper/azen-protocol/introduction.md). # Introduction aZen Protocol transforms computer resources, applications, and AI models into Dynamic Fractional NFTs (dfNFTs). By leveraging smart contracts for scheduling and orchestration. aZen Protocol enables decentralized, scalable, and AI-driven computing environments. This paradigm shift allows not only computational power but also applications and AI tasks to be tokenized, optimized, and executed across a decentralized ecosystem. By integrating resource tokenization, application modularization, and AI-driven computing, aZen Protocol creates an autonomous system where smart contracts dynamically manage and allocate computational workloads. This ensures efficient, secure, and cost-effective execution of AI models, decentralized applications (dApps), and large-scale computational processes. Additionally, aZen Protocol incorporates decentralized data collection and processing, providing AI models with a reliable, distributed, and trustless data infrastructure. This enables more efficient AI training, privacy-preserving computation, and data-driven automation. aZen Protocol is designed to meet this need by establishing a decentralized AI-native coordination framework, ensuring that AI computing, data, and interactions are optimized, self-governing, and economically sustainable. At its core, aZen Protocol provides the rules, mechanisms, and incentives that enable AI agents, computing nodes, and decentralized applications (dApps) to operate in a trustless, interoperable environment. AI-driven applications require real-time, distributed, and intelligent orchestration of resources. aZen Protocol ensures this through: ✅ Autonomous AI Task Management – AI agents execute and coordinate workloads across a decentralized computing network, optimizing performance and efficiency. ✅ Multi-Agent AI Collaboration – AI agents interact, negotiate, and execute tasks through smart contracts, enabling a self-organizing and interoperable ecosystem. ✅ Dynamic AI Governance – AI models continuously adapt to network conditions, ensuring scalability, efficiency, and economic viability.