Allysson-Rodrigues 的 AI 辅助工程工作流与后端开发实践

AI-assisted engineering workflows | Backend systems, tooling, and scoped automation

I build software with a strong bias toward explicit contracts, predictable delivery, and reviewable automation. I am currently studying Systems Development at SENAI PR, with a focus on backend systems, developer tooling, and AI-assisted workflows that stay useful under real engineering constraints.

Principles

• Explicit contracts over implicit behavior.
• Validation should be part of the workflow, not a cleanup step.
• Automation is only useful when scope, permissions, and failure modes are clear.
• Reliable delivery depends on repeatable systems, not improvised heroics.

Workflow

I use AI tooling as part of a disciplined engineering loop rather than as a substitute for judgment. The objective is faster iteration with small diffs, legible decisions, and concrete validation at every meaningful step.

Typical approach:

1. Explore with research-first tooling and map the problem space.
2. Plan with explicit constraints, success criteria, and validation paths.
3. Implement in small, reviewable changes with local verification.
4. Escalate to specialist agents only when the task genuinely benefits from them.
5. Close with concrete checks and a clear record of tradeoffs.

Core surfaces:

Selected Work

• Petshop Small Breeds Premium - full-stack system with admin operations, auth flows, booking requests, and deployment discipline.
• Voice Note AI - Windows-first dictation workflow with Azure Speech-to-Text, safe text injection, and adaptive suggestions.
• Clean Express API - API structure centered on validation, consistent errors, and explicit architectural boundaries.
• Backend TS Foundations - Node.js and TypeScript practice focused on consistency, contracts, and delivery fundamentals.
• TradingView Indicator - Pine Script experiments for structured technical analysis and trading automation.

MCP Surface

I use MCP surfaces where they improve context transfer, reduce manual friction, and keep tool boundaries explicit.

Technical Stack

Roadmap (2026)

• Strengthen end-to-end projects with auth, observability, and deployment discipline.
• Publish sharper backend baselines with better contracts and operational safeguards.
• Keep refining AI-assisted workflows without relaxing verification standards.
• Keep profile and project documentation aligned with active delivery work.

Workspace Snapshot

A live view of my current local workspace architecture, optimized for agentic development:

projetos/
├── 01-projetos/        # Active projects (frontend & backend apps)
├── 02-pacotes/         # Shared packages (e.g., UI kits, libraries)
├── 03-playground/      # Sandboxes and experiments
├── 04-docs/            # Auxiliary or legacy material
├── 05-arquivo/         # Historical, paused, and temporary material
├── 06-scripts/         # Workspace automation and CLI tooling
├── 07-github/          # Local mirror of external repositories
├── 08-operacional/     # Backups, artifacts, and operational logs
├── docs/               # Canonical documentation hub
├── tests/              # End-to-end and governance testing
├── tasks/              # Active sprint, lessons, and progress tracking
├── plans/              # Transient implementation plans (TDP mode)
├── skills/             # Antigravity/Agent extensions and specific toolsets
├── .agent/             # Agent rules and workflows (Antigravity/Gemini/Claude)
└── mcp-servers/        # Local Model Context Protocol servers

Links