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
Field service troubleshooting supports technicians with asset context, manuals, photos, diagnosis steps, parts checks, and escalation.
This covers mobile troubleshooting flows for technicians who need asset context, issue pattern, manual snippets, and escalation on site.
The flow leads from asset to diagnosis questions and evidence, then to fix, escalation, or handoff.
appamass connects React Native/Expo for field work with TypeScript contracts, backend data, and Agent Development Kit (ADK)-based agents for research or diagnosis suggestions.
The first build should guide one common fault safely and show what can be solved on site and what needs escalation.
On site, clarity matters. The flow should show what is known, which check makes sense next, and when the case should be handed off.
Users see asset data, issue description, relevant manual sections, diagnostic questions, needed parts, and escalation path.
React Native/Expo, camera, offline cache, Firebase/FCM, asset records, retrieval, Agent Development Kit (ADK) agent steps, backend APIs, and push connect behind the flow.
Offline data, asset history, spare-part status, escalations, photos, error codes, and service logs stay controlled.
The first build should cover a common failure case and turn it into guided diagnosis.
Technicians find the right asset through scan, search, or location and see current context.
Questions, manual sections, photos, and possible causes appear step by step.
When the case cannot be solved on site, diagnosis, media, and status go to the right team.
Related areas showing how mobile apps, React web systems, AI agents, and controllable automations fit together.