Model Routing Made My AI Agents Cheaper. It Didn't Make Them Easier to Trust.
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Optimizing AI agents involves striking a balance between operational cost and execution trust. While routing tasks to smaller, cheaper models reduces token spend, maintaining output quality requires structured verification frameworks. Using Sol Advisor with Codex, this approach decouples software generation into bounded phases: architecture formulation, implementation, parent verification, and code review. By routing simpler sub-tasks to cost-effective models while placing strict boundary constraints and parent review checks around execution, developers can dramatically lower API expenses without compromising repo safety. For AI-engineering practitioners, this article offers a pragmatic blueprint for context management and cost optimization, proving that trust stems from workflow structure rather than relying solely on frontier model scale.