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 agent control surface shows what agents are doing, which sources and tools they use, what is uncertain, and where humans need to intervene.
This is for teams that need to monitor agent runs, inspect errors, see costs, and route open decisions back to people.
An operator UI makes agent work visible: status, tool use, sources, costs, errors, retries, and intervention points live in one place.
appamass does not think about agents without their interface: Google Agent Development Kit (ADK) and Python run the agent; React/Vite and TypeScript make it operable and auditable.
The first step is a readable run history with steps, result, errors, and the point where a person can take over.
AI agents need a workspace for humans. It has to show what happened, what is waiting, and which decision should remain manual.
Users see run history, tool calls, sources, cost, open decisions, retries, and manual interventions.
React/Vite, TanStack Query, Agent Development Kit (ADK) run models, filters, audit logs, observability, cloud logs, backend contracts, and roles sit behind the surface.
Rights, cost, error classes, retries, manual overrides, approvals, and agent-run evaluation stay controlled.
The first build should make the most important agent runs visible and give the team real intervention points.
Each run shows status, input, intermediate steps, result, error, and duration.
Tool calls, sources, and data access are traceable before a result is reused.
Open cases go to the right role with context, priority, and a clear action.
Related areas showing how mobile apps, React web systems, AI agents, and controllable automations fit together.