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1. Measure token consumption for AI applications at component granularity, and treat token consumption per unit of output quality (T/Q) as a tracked engineering metric.
2. Audit existing workflows for Doing Layer tasks that are still routed through AI, quantify the associated cost, and relocate them to deterministic compute where appropriate.
3. Pilot self-optimizing loops on bounded, low-risk application components, and document both convergence behaviour and realized gains.
4. Publish empirical results, including negative findings, to help strengthen the emerging evidence base for AI architecture optimization.
5. Contribute to open frameworks for agentic architecture optimization so that cost instrumentation, change proposal, automated testing, and quality evaluation can evolve as shared engineering capabilities.
Organizations that develop this loop early are likely to compound efficiency gains over time, as the optimization process continues to operate beyond the cadence of manual intervention.
