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 AI code review and QA pipeline agent supports pull requests, CI results, tests, dependency checks, security hints, and human approval.
This agent supports engineering teams with pull requests, CI results, test gaps, security notes, and review questions.
A QA agent makes PRs easier to review: change, risk, affected files, missing tests, and open review questions become visible faster.
appamass uses agentic coding with TypeScript and Python as a tool, while merge decisions stay tied to tests, architecture rules, CI, and human review.
The first agent should speed up reviews without moving responsibility: explain risk, suggest tests, and make the review point visible.
AI can prepare code review, but it cannot replace responsibility. It helps when it explains changes, highlights risk, and guides reviewers to the right place.
Developers see PR summary, affected files, failed checks, test suggestions, security hints, and approval status.
Repository context, CI/CD, Biome, Vitest, Playwright, dependency data, static analysis, agent comments, and a reviewer surface connect behind it.
Review rules, test coverage, security hints, allowed comments, CI status, approvals, and merge boundaries stay controlled.
The first build should make PRs easier to read and close one concrete gap: risk, test, or approval.
Changes, risks, and affected areas become readable for reviewers.
The agent shows which tests may be missing and which existing checks matter.
CI status, reviewers, security hints, and open questions are visible before merge.
Related areas showing how mobile apps, React web systems, AI agents, and controllable automations fit together.