Enterprise controls on ai coding agents cut software costs significantly
Control the Harness, Control the Cost: Routing and Governing AI Coding Agents in the Enterprise
Artificial IntelligenceCryptography and Security
Summary
Enterprises often use AI coding help from external vendors, which can be expensive if not managed well. The authors developed a smart system called a router that decides which AI model and tools to use for each coding task, helping to lower costs by up to 21%. Their system carefully assigns tasks to models based on price and efficiency, avoiding unnecessary expenses. This approach also helps organizations understand the risks when relying too much on a single AI provider.
What this means in practice
- •For enterprise ai teams: Cut AI coding costs substantially by customizing how coding tasks are routed among multiple AI models in large organizations.
- •For cloud service providers: Design systems to optimize client usage of AI coding agents by implementing dynamic task routing to reduce overall model spend.$Commercial implications: Enables building cost-saving services for enterprises buying AI coding capabilities at scale.
Authors
Arian Abbasi, Alan Aqrawi, Ted Kwartler
Abstract
Harnesses, the products that run AI coding agents, are multiplying, and enterprises are rolling them out to their employees: what started as pilots with a few hundred seats is scaling to tens of thousands. Most enterprises do not build these harnesses but buy them from large vendors, such as Anthropic's Claude Code or OpenAI's Codex. A harness decides which model answers, what the model reads, how the prompt cache is used and which subagents run, so it picks the rate on the price sheet and sets the volume bought at it. Enterprises that keep a proprietary or untuned harness at its defaults inherit these choices and their bill. We build a fast, customisable router in which Jev, a classifier with calibrated probabilities, labels every prompt against a bring-your-own taxonomy of agentic requests. Because one user turn is many requests over a prompt cache that belongs to one model, the router moves work only where no running conversation has to rebuild its cache: at session start, in side lanes and at subagent launch. From the price sheet we derive when a mid-task switch pays back, and a crossover: on long tool-heavy sessions the highest-priced model costs less than the next tier, as repricing about 10,000 real sessions from public datasets confirms. In an emulated enterprise of 10,000 seats with user behaviour taken from these datasets, the router recovers 14 to 21% of model spend at Anthropic's list prices of 21 September 2026, \$3.3M to \$5.0M a year. The paper also maps the risks across twenty harnesses, prices the dependence on one vendor's models, and proposes a control plane that enterprises can run from within, starting now, with a ladder for deciding later whether to own the harness.