The hidden AI cost driver: Harness design can make or break enterprise agent economics

AI consultancy Systima found that agent harness configuration can significantly inflate token overhead and costs in enterprise AI deployments, with tests showing up to 38% token reduction by changing harness design alone. Researchers and analysts warn that enterprises overlook harness orchestration, focusing instead on model pricing, leading to hidden cost drivers as agents scale to production.

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 blog post https://systima.ai/blog/claude-code-vs-opencode-token-overhead . Separately, the firm also found that other configuration choices while setting up the harnesses such as repository instruction files, Model Context Protocol https://www.infoworld.com/article/4029634/what-is-model-context-protocol-how-mcp-bridges-ai-and-external-services.html 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 paper https://arxiv.org/pdf/2607.06906 , titled The Harness Effect: How Orchestration Design Sets the Token Economics of Enterprise Agentic AI, researchers showed that changing the harness while keeping models and tasks the same can reduce token consumption by 38%, cost per task by 41%, and execution time by 44% while maintaining comparable quality. Analysts say that enterprises can gain greater control over AI agent operating costs by paying closer attention to how their harnesses are configured and orchestrated, instead of just relying on model pricing as a yardstick. “The evaluation shows that the model is only one part of agent economics. The harness, tool schemas, instructions, MCP connections, and subagents matter as well. Enterprises therefore need to measure the entire agent configuration, not assume model pricing tells them what an agent will cost,” said Stephanie Walter https://www.linkedin.com/in/slwalter , 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 Advait Patel https://www.linkedin.com/in/advaitpatel93/ , site reliability engineer at Broadcom. “Ask the average CIO whether their coding agent rewrites its cache mid-session, and you will get a blank stare,” Patel added. However, Ashish Chaturvedi, executive research leader at HFS Research, pointed out that lack of visibility is less a failure of enterprise leaders than a consequence of how AI agent ecosystem components are sold, stacked, and managed presently. “Most organizations have no visibility, mainly due to the absence of any metric from the vendor’s end that lets CIOs measure the entire agent or at least the harness configuration. None of this shows up in the developer’s experience. The agent just works, and the tokens burn silently in the background,” Chaturvedi said. The problem is further compounded, according to Chaturvedi, due to the manner in which AI agent configuration is distributed across enterprise teams. “The harness is chosen by one team, the instruction file written by another, and the MCP servers attached by a third, so no single person sees the cumulative weight,” Chaturvedi noted. Even when, in some cases, enterprises do have visibility and ownership, Patel argued, the industry, in general, still lack the operational maturity and discipline to systematically optimize AI agent costs. “FinOps for agents is where cloud FinOps was in 2013. Nobody has hired the equivalent of a cost optimization team focused on prompt engineering, harness configuration, and cache stability,” Patel said. Separately, Abhishek Satapathy https://www.linkedin.com/in/abhisekhsatapathy/ , 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 https://arxiv.org/pdf/2601.14470 , 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. 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.”