Case Study · Personal / Open Innovation

Evolvia — Personal AI OS & Multi-Agent Platform

Autonomous multi-agent orchestration operating under strict architectural playbooks.

Evolvia Personal AI Operating System — Win Naing Soe project case study
Role
AI Systems Architect & Backend Lead
Period
2026 — Present
Client
Personal / Open Innovation
Domain
Autonomous Agents · AI Orchestration · Developer Tools
/ Overview

Architected a Personal Life Operating System powered by autonomous multi-agent orchestration. Operating with Claude Code CLI, custom subagents, and Model Context Protocol (MCP), Evolvia enables AI agents to plan, execute, and verify tasks against strict operating manuals (AGENTS.md, CLAUDE.md) with sandbox safety and token discipline.

/ Problem & Challenge

Autonomous coding agents frequently suffer from hallucinated dependencies, schema drift, and context-window degradation when executing multi-step complex tasks without strict operational boundaries.

/ Architecture & System Decisions

Engineered comprehensive agent operational playbooks and release gates, enabling autonomous task decomposition (GSD mode) with deterministic verification test harnesses and isolated context spaces.

/ Engineering Trade-offs
⚖ TRADE-OFF DECISIONRole-Segregated Subagents vs Monolithic Prompting: Used isolated subagents for database administration, architecture, and testing, isolating each agent's context window to prevent reasoning degradation.
/ Impact

Engineered comprehensive agent operational playbooks and release gates, enabling autonomous task decomposition (GSD mode) with deterministic verification and zero hallucinated schema drifts.

/ Highlights
  • ◆Authored exhaustive AGENTS.md & CLAUDE.md operating manuals defining agent roles, permission boundaries, and quality gates.
  • ◆Configured Model Context Protocol (MCP) servers for isolated file operations, PostgreSQL schema inspection, and Playwright verification.
  • ◆Integrated Subagents (Architecture, DB Administrator, QA Bot) for segregated responsibility and context-window optimization.
  • ◆Established token budgeting and LiteLLM model routing to balance execution latency against reasoning costs.