Arin's Tech Musings

Honest disclaimer: The thoughts here are mine — the prose, structure, and general readability are courtesy of AI. I handed it a brain-dump and it handed back something you'd actually want to read. Felt appropriate, given the topic. Most teams building agentic workflows skip the architecture work and jump straight into building. They don't think through the boundaries first—what orchestration owns, what each capability should do independently, where standards live. So the first agent they write becomes a dump for everything: orchestration, logic, policy, approval, output formatting. All in one. It works until it doesn't. This post is about why that happens, what breaks when it does, and a practical architecture that scales: thin orchestrator agents, focused Skill units, clear Instructions , and predictable output paths. In GitHub Copilot (and similar agent harnesses), the goal is to keep orchestration, capabilities, and standards decoupled as systems scale. It's s...

Honest disclaimer: The thoughts here are mine — the prose, structure, and general readability are courtesy of AI. I handed it a brain-dump and it handed back something you'd actually want to read. Felt appropriate, given the topic. GitHub Copilot's customization system separates into four distinct layers: custom agents, instructions, skills, and MCP servers. Each handles a different concern, and they compose together while remaining independently upgradeable. The key insight: separating "who" (agent workflow), "rules" (project standards), and "how" (capabilities) means you can change one without touching the others. Update standards without rewriting agents. Upgrade skills without modifying instructions. Change agent behavior while keeping conventions intact. Everything lives in git. Changes go through pull requests. You can review, approve, and roll back AI behavior like infrastructure. GitHub Copilot Architecture Custom Agents Who orc...