Structure and Significance of Modular L2 Technology Combining Prompt-Based dApp Generation and Affective Intelligent AI Agents @Kindred_AI, @CodeXero_xyz, @0xPolygon The way decentralized applications are created in the blockchain environment is gradually shifting from code-centric development to methods that interpret user intent. This change is driven by real constraints such as the specialized knowledge required for smart contract development, audit costs, and the complexity of upgrades. To alleviate these, application generation technologies using natural language prompts have emerged. Prompt-based dApp generation technology operates by mapping user-expressed goals in everyday language to predefined logical structures and templates, converting them into an on-chain executable form. The key in this process is clearly distinguishing the boundaries between user intent, the generated logic, and the deployed smart contract. Intent remains at the natural language level, logic is confined to a verifiable format, and the deployment result is fixed as deterministic code. An example of this approach is the prompt-based dApp generation engine proposed by CodeXero. CodeXero's system constructs applications by selecting and filling in parameters of predefined and verified templates, rather than creating entirely new code from user input. This can be understood as a conservative design to reduce potential errors and vulnerabilities in the automated generation process. According to currently available information, intermediate format specifications and automated verification tools are used in a limited manner. Nevertheless, this template-based generation method treats prompts as an infrastructure-level input and plays a role in lowering the initial entry barrier for application development. If prompt-based generation technology serves as a conduit for translating user intent into code, affective intelligent AI agents play a role in refining user intent more accurately at an earlier stage. The affective intelligence agent architecture proposed by Kindred Labs analyzes various inputs such as text, voice, and gestures to detect the user's emotional state, structuring it into a multidimensional model of arousal, emotional valence, and sense of control. This system goes beyond simple emotion classification by using a long-term memory processing unit to store and manage user preferences, past decisions, and emotional contexts in the form of a knowledge graph and embeddings. This structure increases contextual understanding with each interaction with the user, while information that is outdated or unnecessary is removed according to DAO policies. Affective intelligent AI agents act as intermediaries in the prompt-based dApp generation process. They detect hesitation or confusion when a user requests application creation, refine the expression, and induce additional confirmation before irreversible actions are taken, thereby organizing the intent. The purpose of this process is to stabilize input at the stage before code generation, without altering the code itself. Therefore, design boundaries are set so that the results of affective analysis do not directly influence the logic of the smart contract, which is understood as a structural safeguard to reduce the risk of emotion-based decision-making in financial contexts. For this prompt generation and affective mediation structure to function in practice, a stable execution environment is required, which is provided by modular L2 architecture. Polygon's modular L2 structure allows specific dApps or AI mediation logic to be placed in an independent execution environment through the CDK that creates application-specific rollups. In this environment, state transitions are verified through zero-knowledge proofs, and liquidity and interoperability between multiple rollups are maintained through AggLayer. Additionally, an upgrade mechanism based on the proxy pattern allows only changes following verified procedures. This structure restricts AI agents from performing non-deterministic actions and ensures that generated code always produces the same result for the same input. As prompt-based generation, affective AI, and modular L2 execution environments combine, the importance of governance and standardization also becomes prominent. Parameters of the affective engine and memory management policies are managed through governance structures such as Kindred DAO, and at the individual agent level, AS-DAO adjusts detailed behaviors. Code generation templates and execution layers are also controlled through their respective governance systems, with change histories tracked and control mechanisms such as emergency halts and staged deployments in place. As a standard for trust and interaction supporting this ecosystem, the ERC-8004 and x402 protocols are utilized.ERC-8004 defines AI agents as identifiable on-chain entities, including NFT-based identity, reputation scores, and verification methods. This allows the tracking of agent behavior history and enables trust adjustment for abnormal behavior. The x402 protocol defines a payment flow based on HTTP status codes, automating micro-payments between AI agents or between agents and services. This enables settlement for detailed work units such as prompt generation, sentiment analysis, and API calls. Ultimately, the integration of prompt-based dApp creation technology and sentiment-intelligent AI agents on a modular L2 results in a structure that interprets user intent in natural language and emotional context, connecting it to verified logic and deterministic execution. This structure operates by increasing accessibility through automated code generation, while maintaining the boundary between sentiment analysis and execution logic, ensuring safety and traceability through a modular architecture and standardized trust layer. $KIN $SEI $POL