AI Agent Development
AI agent development becomes useful when an agent does more than answer: it uses sources, tools, rules, and approvals to complete a task.
Product systems and software architectures with Google Agent Development Kit (ADK), React Native & React
React Native / Expo AI apps connect mobile inputs, camera, secure data, push, and agent steps into an app that helps users on the move.
This fits mobile apps that capture photos, text, location, or scans and turn them into an AI-supported recommendation or action.
The mobile flow captures input, shows uncertainty, and lets the agent go only as far as the user can review.
appamass combines React Native/Expo, TypeScript, Google Agent Development Kit (ADK), and Python so mobile app, web review surface, and agent logic share one workflow.
A useful start is a field-ready flow with input, agent suggestion, correction, and controlled completion.
AI in a mobile app should not only chat. It should use the moment: camera, location, input, and context become a reviewable next step.
Users see detected information, editable fields, sources, follow-up questions, and the choice to save or submit an action.
React Native, Expo, TypeScript, Agent Development Kit (ADK) events, Firebase Auth/FCM, secure storage, deep links, backend APIs, and push connect behind the flow.
Local data, permissions, sources, uncertainty, push rules, error cases, and approvals stay controlled.
The first flow should show how a user captures something on the move and how an agent prepares a helpful, reviewable action.
Photo, text, location, or scan is captured in a structured way with user context.
The suggestion shows detected fields, sources, uncertainty, and correction options.
Save, hand off, escalate, or submit becomes a clear controlled step.
Related areas showing how mobile apps, React web systems, AI agents, and controllable automations fit together.