Enterprise AI faces a return on investment paradox where powerful foundation models are prohibitively expensive in production. Researchers propose optimizing the AI harness, the orchestration layer around the foundation model, as a solution. By refining components like prompt caching and interaction history compaction, they achieved significant cost reductions without compromising quality. This approach allows engineering teams to build cost-efficient AI applications without fine-tuning the underlying models. The current industry trend of "tokenmaxxing" wastes resources by relying on large context windows instead of efficient system design. This brute-force method treats token costs as negligible, masking underlying inefficiencies that compound over time. Existing efficiency techniques like prompt compression fail because they optimize only parts of the system, ignoring the orchestration layer. The harness, historically treated as disposable code, is now recognized as crucial for controlling AI costs. Optimizing the harness involves system prompt caching, interaction history compaction, tool management, retrieval strategies, and error management. Experiments demonstrated that optimizing the harness reduced cost per task by 41% and token consumption by 38%. Task success rates remained steady, and end-to-end latency significantly decreased. Developers can implement optimizations like the "Two-Zone Prompt" for caching and "Context Offloading" to manage context effectively. Building resilient loops with hard checks on token budgets and generation limits is essential to avoid runaway costs. As foundation models evolve, the harness will shift from compensating for model weaknesses to enforcing enterprise policies like budgets and data boundaries.