AI Native

Prerequisite: Familiarity with the concepts introduced in Part 5 — Campaign Architecture. Review it first if the terminology in this part is unfamiliar. Answer-first: Integrating AI capabilities into payment platforms involves embedding real-time LLM RAG hubs for customer support and ML fraud detection models into transaction evaluation pipelines, enforcing sub-20ms model inference SLAs. Answer-first: PayPay integrates AI into its transaction pipelines by streaming payment events asynchronously to machine learning scoring models. Running inference out-of-band prevents risk assessment evaluations from adding latency to the synchronous checkout flow, enabling real-time fraud detection and dynamic credit scoring. ...

Generative UI with MCP: Architecting AI-Native Frontends Generative UI with Model Context Protocol (MCP) moves frontends from text-only chat interfaces to dynamic, interactive UI components. Using React Server Components, Zod runtime schema validation, dynamic component registries, and iframe sandboxing, AI agents can safely trigger rich native UI components directly from structured tool call outputs. This post covers six pieces of that architecture: moving beyond plain-text chat, reconciling agent reasoning state with live DOM state, managing a versioned component registry, securing and making components accessible, human-in-the-loop confirmation for high-risk actions, and testing/deploying at the edge. ...

Prerequisite: Familiarity with the concepts introduced in Part 8 — The Junior Paradox. Review it first if the terminology in this part is unfamiliar. Answer-first: Building an AI-Native Architecture requires refactoring traditional backend systems from static monolithic REST endpoints into modular Domain-Driven Design (DDD) bounded contexts exposed via standardized AI protocols (MCP / gRPC). This enables autonomous agents to inspect, reason over, and execute application capabilities dynamically under zero-trust security. ...