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
AI product integration adds summaries, recommendations, prefilling, or review to existing apps without breaking the current user flow.
This fits existing products where summarization, prefill, review, or recommendation should help exactly inside the current workflow.
AI becomes a product feature in the existing screen, not a second interface that pulls users away from their work.
appamass connects existing React or React Native flows with TypeScript contracts and Agent Development Kit (ADK)-based agents so AI suggestions stay traceable and switchable.
The starting point is an existing user task with limited data access, visible source, and an easy way to correct the suggestion.
AI in an existing product helps when it makes one concrete step easier. The integration should not create a second product beside the real one.
Users stay in the familiar surface and see what AI prepared, which source supports it, and what can still be changed.
Existing React or React Native frontends, backend APIs, roles, feature flags, Agent Development Kit (ADK) tools, logging, and review surfaces need to work together cleanly.
Feature rollout, data access, user rights, feedback, cost, errors, and the ability to disable AI features stay controlled.
The first slice should improve one existing user task and make clear what AI prepares and what the human decides.
Pick a place where users already read data, compare options, or prepare a decision.
The agent gets only the sources and actions needed for this step.
Users can correct or reject suggestions and improve the quality of the flow.
Related areas showing how mobile apps, React web systems, AI agents, and controllable automations fit together.