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
Generative UI makes agent results visible as reviewable surfaces: tables, forms, cards, comparisons, approvals, and corrections.
This is about agent results that do not end as long chat text, but appear as editable tables, forms, cards, warnings, or approvals.
Generative UI turns a suggestion into a surface: users can edit fields, open sources, compare variants, and approve the next action.
appamass connects Agent Development Kit (ADK) events and structured outputs with React/Vite and Tamagui so agent logic becomes a reviewable product interface.
The output schema has to be stable first; only then can the UI render, validate, and provide useful fallbacks safely.
Many agent results become useful only when they leave the chat box. A good surface makes suggestions editable, comparable, and approvable.
Users can open sources, change fields, compare suggestions, reject a step, or approve a prepared action.
Agent Development Kit (ADK) events, structured output schemas, React/Vite, Tamagui components, validation, roles, backend contracts, and fallbacks for uncertain agent results connect behind the surface.
Schema versions, required fields, validation, manual corrections, audit logs, and fallbacks stay controlled when the agent cannot produce a safe result.
The first slice should turn one concrete agent result into a surface a human can edit.
The agent returns structured fields instead of free text so the UI can render and validate safely.
Users see suggestion, sources, editable fields, warnings, and approval in one flow.
Incomplete results, uncertain sources, and validation errors get clear correction paths.
Related areas showing how mobile apps, React web systems, AI agents, and controllable automations fit together.