The race to build better AI software has largely focused on the model: which one reasons better? Codes better? Costs less? At Adronite, our testing points to another major efficiency lever that gets far less attention: the harness surrounding the model.
Our internal benchmarking suggests the choice of harness can materially change the economics of an AI coding task, even when the underlying model, prompt and tooling remain exactly the same. By changing how context is assembled and delivered to the model, we saw meaningful reductions in both token consumption and cost, pointing to an efficiency opportunity that has little to do with choosing a different or cheaper model.
As enterprises move from AI coding experiments to long-running agentic workflows, models don’t work in isolation. They depend on a harness to retrieve files, logs, metadata and project information and determine what information the model sees at each stage of a task. How intelligently that context is assembled can directly affect how many tokens the model consumes, how accurately it understands the codebase and, ultimately, how much the work costs.
Bigger Context Windows Don’t Solve the Context Problem
Frontier models can now process enormous amounts of information. But the ability to consume more context doesn’t mean a model should consume everything available.
Coding harnesses can repeatedly load files and fragments to make sure the model has enough information to complete a task. That approach can quickly fill even very large context windows with information that may have little relevance to the problem being solved. More tokens are consumed, costs rise and the model still has to determine which information actually matters. The better question is how much of the right context we can provide any given model.
For software development, that means understanding relationships across the codebase: architecture and component boundaries, dependencies between files and services, existing standards and constraints and the developer’s intent. A model that can connect those dots at the right moment may need far less information to reach the same, or potentially a stronger, result.
The Harness Is Becoming an Intelligence Layer
This changes how we should think about AI coding infrastructure. A harness shouldn’t act as a delivery mechanism that stuffs information into a model’s context window. It should understand the environment well enough to determine which information matters for the task at hand.
That’s the principle behind the Adronite Context Engine (ACE) underlying our Codistry platform. ACE maintains an updated relational view of a codebase, mapping its architecture and dependencies as work progresses. Rather than repeatedly loading large portions of a repository, it supplies the model with the context most relevant to the task.
There is a straightforward objective here – give the model less information, but make that information more useful. That can reduce irrelevant tokens, repeated prompting and context drift while helping the model understand even very large production codebases.
Same Model, Nearly Half the Cost
That’s the premise we were testing when we benchmarked Codistry. The tests used identical prompts, tooling and model configurations. Context is the only component that changed. Across comparable development tasks, Codistry used roughly half the tokens and delivered approximately 48% lower cost per task. On one test, per-task cost declined from $2.12 to $1.10.
But token efficiency is only part of the equation. Getting the result right the first time matters the most. Every hallucination, broken dependency, repeated test and corrective prompt adds another layer of tokens, developer time and cost. Better context has the potential to improve both sides of that equation, reducing what the model needs to process while giving it a better understanding of what it’s being asked to do.
Stop Looking Only at the Model
For enterprises deploying AI coding at scale, the implications extend beyond the savings from a single task. Teams will continue switching among frontier, specialised and self-hosted models as capabilities, economics and workloads change. Optimising solely around today’s best-performing model therefore creates a moving target.
The harness can provide a more durable layer of intelligence. If it understands the codebase and can deliver the right context to whichever model a team chooses, enterprises can improve efficiency without tying their development strategy to a single model provider. As agentic coding becomes more autonomous and long-running, that capability will become increasingly important.
The next leap in AI coding efficiency may not come from another jump in model intelligence. It may come from being much smarter about what we provision the model to see.