✅ Built multi-agentic harness architecture compatible with GitHub Copilot Agents and Claude Code
✅ Applied and validated across multiple personal and client projects
✅ Became the personal foundation for Gen-e2 methodology R&D at PALO IT
What is this project about?
Before Gen-e2 methodology had a name, I was already building the thing. AI-Gile is my personal implementation of an AI-enhanced product development lifecycle — a multi-agentic harness architecture that runs on either GitHub Copilot Agents or Claude Code, orchestrating the full PDLC from requirements through delivery.
The core idea: structured context engineering at every layer. Not just code generation — but how requirements are captured, how tasks are broken down, how reviews happen, and how decisions get made. The model handles execution. The human handles orchestration and judgment.
What started as personal experimentation eventually became the foundation for how I approach AI-powered delivery on client projects. The bottleneck was never the model — it was always how precisely I could articulate what I wanted.
Takeaway #1: 🧠 The model isn't the bottleneck
When AI output isn't good enough, the instinct is to blame the model. Almost always, that's wrong. The quality of the output is a direct reflection of the quality of the input — the context, the structure, the instructions.
Invest in how you talk to the model, not just which model you use.
Takeaway #2: 🔁 Personal frameworks compound
Every iteration of AI-Gile fed directly into how I approached the next project. The lessons from Pocket Chiro informed the work at MTR. The MTR lessons informed Gen-E2 training at OOCL.
Build your frameworks in the open, even if the "open" is just your own next project.
🛠️Tech Stack
Project Management
