The Harness Effect: How Orchestration Design Sets the Token Economics of Enterprise Agentic AI
Agentic AI development today runs on token maxing: buying capability with tokens -- longer reasoning traces, more turns, wider tool payloads, bigger replayed contexts -- so tokens per task grow faster than task value. Falling per-token prices mask the pattern; total spend rises anyway. We argue the decisive lever against token maxing is the harness: the orchestration layer that assembles context, exposes tools, sequences turns, delegates work, and carries enterprise observability and governance. We isolate it with a controlled swap: 22 locked evaluation tasks, six foundation models (Claude Sonnet 4.6, Gemini 3.1, Gemini Flash 3.5, Qwen 3.6, GLM 5.1, Palmyra X6), changing only the orchestr...
| Authors | Muayad Sayed Ali, Aliaksandra Novik, Anji Boddupally et al. |
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| Primary category | cs.AI |
| Published | 8 July 2026 |
| Last updated at source | 5 October 2026 |
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