Model Routing Cuts AI Agent Costs but Trust Requires a Separate Workflow

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A developer found that routing AI tasks to cheaper or more capable models based on complexity reduced running costs for multiple agents, but did not resolve the harder problem of trusting the output. Even when an agent reported a task complete with passing tests, there was no guarantee it had addressed the right problem or avoided unintended changes elsewhere in the codebase. To address this, the developer adopted a structured workflow using Sol Advisor with Codex, separating architecture, implementation, and review into distinct, bounded stages. Each delegated task is accompanied by a detailed brief covering objectives, scope, constraints, and expected verification evidence, preventing agents from producing technically valid but contextually wrong solutions. The key insight is that an implementation report is a claim, not proof, and human inspection plus independent review remain essential steps before shipping any patch.

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