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
An enterprise knowledge workspace turns documents, tickets, wikis, policies, and records into citable answers and working notes.
This is internal knowledge work with documents, tickets, wikis, policies, or datasets where every answer needs a visible source.
A knowledge workspace returns answers with citations, source cards, freshness, and correction paths instead of detached chat responses.
appamass connects retrieval, Agent Development Kit (ADK)-based agents, TypeScript contracts, and React/Vite interfaces so knowledge becomes a reviewable workspace.
The first knowledge area should be small enough to review source quality, permissions, and feedback properly.
A knowledge surface earns trust through sources. Users should not only get an answer, but understand where it came from and how current it is.
Users see search, answer, citations, source cards, freshness notes, follow-up questions, and correction options.
Retrieval, source metadata, citation surface, IAM permissions, Agent Development Kit (ADK) tools, Firestore or Cloud SQL, evaluation, feedback, and data quality connect behind it.
Access rights, source freshness, citation quality, corrections, missing documents, evaluation, and governance stay controlled.
The first build should cover one knowledge area with clear sources and make answers consistently verifiable.
Only relevant documents, wikis, tickets, or policies enter the first search space.
Each answer shows passages, source, freshness, and uncertainty.
Users can mark wrong, missing, or outdated information.
Related areas showing how mobile apps, React web systems, AI agents, and controllable automations fit together.