A largely overlooked layer of the AI stack is emerging as a major driver of enterprise costs. New testing by AI consultancy Systima found that agent harnesses, the software that coordinates models, tools and workflows, can generate significant token overhead through their configuration alone, potentially inflating the cost of AI deployments as organizations scale agents from experimental pilots to production environments.
The firm, which ran a series of tests by juxtaposing two harnesses on the same tasks, namely Anthropic’s Claude Code and open-source OpenCode using the same Claude Sonnet 4.5 model underneath, found both exhibiting sharply different token overhead because of the differences in their configuration.
These differences included system prompts, tool definitions, agent coordination mechanisms and other orchestration components, resulting in markedly different baseline input token overhead before users even entered a prompt, the consultancy firm wrote in a (MCP) servers, prompt framework templates and subagents can each add substantial token overhead.
The consultancy’s conclusions are also supported by emerging academic research examining how orchestration of the harnesses themselves, rather than optimizing models or changing them, can help enterprises reshape the economics around AI agents.
In a , practice lead of the AI stack at HyperFRAME Research.
Currently, most enterprises pick agent tooling based on model quality, benchmarks, developer experience, and headline pricing per seat or per million tokens, with almost no one measuring what the harness sends per request, how stable the cache prefix is, or what subagent fan out costs at scale, echoed , principal analyst at Avasant, pointed out that the invisibility issue stems from how enterprises evaluate AI agents before deploying them into production: “Most proof-of-concepts involve a limited number of users, relatively short-lived sessions, and controlled agentic interactions, where the accuracy of model output is the primary evaluation criterion.”
The analysts’ comments also echo the conclusions of another research paper, in which researchers argued that token consumption in agentic software engineering systems remains poorly understood because existing metrics provide limited visibility into where tokens are spent across orchestration components.
How CIOs can improve visibility into AI agent costs
Closing that visibility gap, though, according to Satapathy, is increasingly becoming a priority for enterprises, as AI agents move from pilots to production and operating costs become harder to predict.
“Across our advisory engagements, we are seeing growing demand for AI observability frameworks that combine runtime tracing, workload-level cost attribution, and execution analytics. This enables organizations to establish engineering baselines, benchmark workload efficiency, forecast AI operating costs, and continuously optimize agent performance as deployments mature,” Satapathy said.
However, until vendors provide more comprehensive visibility into harness-level token consumption, analysts said enterprises should begin treating harness configuration as an operational governance issue rather than merely a developer preference.
“The single most valuable move is to get visibility into what the harness actually sends. Enterprises should treat configuration as a governed cost decision, deliberately match harnesses to workloads, and closely monitor cache behavior and subagent fan-out, since those were among the biggest cost multipliers identified in the evaluation,” Chaturvedi said.
Walter echoed that recommendation, saying CIOs should require observability across the entire agent configuration: “Without that visibility, enterprises are effectively buying an agent platform without knowing how much of the bill comes from useful work versus orchestration overhead.”
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