How do I know if code quality controls are reducing AI spend?

Why This Matters for Security Teams

AI spend can rise quietly when code quality is poor, because the agent needs more context, more retries, and more tool calls to complete the same task. For teams using LLMs in software workflows, the cost problem is not only model usage. It also includes failure loops, reranking, repeated retrieval, and human intervention. That makes code quality a control issue, not just a developer preference. The NIST Cybersecurity Framework 2.0 is useful here because it treats governance, measurement, and continuous improvement as operational disciplines rather than one-time checks.

The practical question is whether structural improvements in the repository actually reduce the amount of work the AI has to do. If a refactor makes modules clearer, dependency paths shorter, and tests easier to interpret, the agent should need fewer rereads and fewer turns to reach the same outcome. That is the signal worth measuring. Without it, organisations may celebrate cleaner code while the AI layer still burns budget on navigation overhead and repeated context assembly. In practice, many security and engineering teams notice the spend problem only after agent workflows have already become noisy and expensive, rather than through intentional cost instrumentation.

How It Works in Practice

Start by treating AI spend as a measurable outcome of code interaction quality. Compare the same workflow before and after a structural change, then normalise for task size. If the agent is reviewing a pull request, generating a fix, or tracing a defect, the relevant indicators are token usage, reread frequency, turn count, retrieval depth, and task success rate. Lower usage is only meaningful if the pass rate or acceptance rate stays stable.

A useful operating model is to separate signal from noise:

• Measure a baseline across several similar tasks, not one lucky run.
• Track whether the agent revisits the same files or instructions multiple times.
• Check whether shortened prompts and cleaner interfaces reduce the number of corrective turns.
• Compare cost per completed task, not just raw token volume.
• Validate that fewer tokens do not lead to weaker output quality or hidden rework.

This is where AI governance and software hygiene overlap. Good code quality reduces ambiguity, and reduced ambiguity cuts down the model’s need to infer missing structure. That matters in agentic workflows because the agent can only act efficiently if the repository is easy to traverse and the tool results are easy to trust. The NIST AI Risk Management Framework is relevant because it pushes teams to evaluate whether AI outputs are reliable, traceable, and fit for purpose, while OWASP guidance for LLM applications helps teams think about prompt and output failure modes that can inflate runtime cost.

For practitioners, the cleanest method is to instrument a before-and-after control group. Use the same benchmark tasks, the same model, and the same toolchain, then compare the average cost per accepted result. If code quality controls are working, the agent should spend less time resolving ambiguity and more time on the actual task. These controls tend to break down when the repository has many hidden dependencies, inconsistent abstractions, or poorly defined tests because the model keeps re-reading the same context and cannot confidently stop.

Common Variations and Edge Cases

Tighter code quality often increases upfront review effort, requiring organisations to balance short-term engineering overhead against lower AI runtime cost. That tradeoff is real, and current guidance suggests it should be judged by measured outcome, not intuition. A refactor that reduces context size may still increase spend temporarily if it breaks tests, forces prompt rewrites, or changes tool outputs in ways the agent cannot adapt to quickly.

There is no universal standard for this yet, so teams should avoid treating one metric as decisive. Lower token usage can be misleading if the agent is making shallow progress, and lower reread counts can be irrelevant if the task itself became easier. The best practice is evolving toward a bundle of indicators: cost, pass rate, repair rate, and the number of times an agent has to recover from an incorrect assumption. For AI-heavy engineering environments, this is especially important when code generation, retrieval, and CI feedback are tightly coupled.

Edge cases appear in monorepos, legacy systems, and highly dynamic codebases where structural changes alter tool behaviour more than code clarity. They also appear when agents rely on external documentation, because improved repository quality may not reduce spend if the real bottleneck is inconsistent specs or unstable APIs. In those situations, teams should measure the full workflow, not just the repository. The OWASP LLM guidance remains useful for distinguishing prompt-driven failure from code-driven failure, while the NIST Cybersecurity Framework 2.0 reinforces the need for continuous monitoring and control verification.

Related resources from NHI Mgmt Group

• How do you know if AI access controls are actually working?
• How do teams know whether AI prompt controls are actually working?
• How do you know if code signing controls are actually working?
• How do organisations know whether DORA controls are actually covering AI risk?