Controlling AI Coding Costs Through Engineering Discipline

AI coding costs can become unpredictable when agent usage is managed like conventional SaaS rather than an engineering workload. The article identifies three recurring sources of waste: oversized context, tool-generated noise, and expensive models used for routine tasks. It also argues that tightly coupled legacy architectures make agents slower, less reliable, and more costly. The proposed response combines telemetry, context management, model selection, caching, and codebase modernization. Technology leaders should measure AI cost against completed engineering outcomes and treat architecture quality as part of AI economics.