Introducting Spec-Agents

The Research That Changed Everything

When I started researching AI coding agents for this post, I had a simple goal: find the best tool to accelerate my solo development practice. I spent weeks testing every agent mentioned above—Aider, Claude Code, Plandex, Codebuff, OpenCode. I dogfooded them on real projects. I tracked costs meticulously. I measured quality.And I hit a wall every single time. Not because the tools were bad. They're excellent. But because I kept running into the same fundamental problems:

• The modification trap: I'd think "I just need to add X feature to Aider" and spend three days deep in 10,000 lines of Python instead of shipping features for clients.
• The cost explosion: Using premium models (Claude Opus, GPT-4) for everything was costing me $900/month. For a solo developer, that's untenable.
• The quality vs. cost dilemma: Cheaper models saved money but produced code that needed extensive revision. I was trading dollars for hours—not a win.
• The local model challenge: I had two RTX 3090s sitting idle while paying for cloud APIs. Running local models manually worked, but there was no intelligent orchestration.

The tools I reviewed are solving important problems. But they weren't solving my problem: How does a solo developer get premium AI assistance at sustainable costs while maintaining code quality? The Insight: Orchestrate, Don't Modify The breakthrough came from an unexpected place: reading about special operations teams. Elite military units don't have one super-soldier who does everything. They have specialized operators: a sniper, a medic, a demolitions expert, a communications specialist. Each is the best at their specific role. The team's power comes from intelligent coordination, not individual capability. What if AI coding agents worked the same way? Instead of trying to make one agent perfect at everything, or modifying complex tools I didn't fully understand, what if I built a lightweight orchestration layer that:

Routed tasks intelligently to the right model for the job (cheap models for simple tasks, premium models only when needed) Used local models by default (my GPU hardware = free inference) with cloud fallback for critical work Enforced quality standards through a "constitution" (like GitHub Spec Kit's spec-driven approach) Tracked costs in real-time with hard budget limits Required no modifications to the underlying agents—just coordinated them

What I Built: Spec-Agents Spec-Agents (specifications + special operations) is an open-source orchestration framework I built to solve this problem. It's not another AI coding agent—it's a command center for the agents you already use. The architecture: