Building an Agentic AI Assistant for React Native

Short summary

The article outlines a production architecture for agentic AI assistants in React Native, where the LLM handles reasoning and planning while the application enforces safety through validation, confirmations, and controlled database writes. Key principles include building capabilities as modular skills, never letting the model write directly to the database, returning errors to the model for natural disambiguation, and keeping raw data out of prompts for privacy and cost efficiency. The author also covers mobile-specific challenges like offline mode, battery consumption, and API rate limits that most tutorials ignore.

• •Separate responsibilities: model handles reasoning and planning, application handles validation, permissions, and safety
• •Build capabilities as modular skills with their own tools and validation logic rather than one giant system prompt
• •Never let the AI write directly to the database — stage changes, confirm with user, validate, then save
• •Return errors to the model instead of hiding them to enable natural conversational disambiguation
• •Keep raw database rows out of prompts; return only IDs, labels, counts, and status for privacy and cost savings

Generated with AI, which can make mistakes.