How do you reduce AI coding agent costs without losing quality?

Engineering teams seeking to reduce AI agent costs should prioritize cost per merged pull request rather than token usage, while also tracking human intervention and defect or rollback rates to prevent quality regressions. The proposed sequence of cost controls begins with routing task classes such as dependency updates, UI fixes, and test additions to models that have demonstrated adequate performance at lower cost, followed by reducing unnecessary context, limiting retries and idle compute, and introducing verification earlier in workflows. Changes should be tested through replaying real tasks from identical repository states, using consistent scoring and repeated runs to account for variability, with human review as an additional safeguard. The text argues that broad reductions in model quality, ignoring time-based compute charges, failing to update routing as models evolve, and measuring savings without quality controls are common mistakes. It presents Warp Factories as a platform that stores run-level metrics and supports routing, benchmarking, replay, scoring, and reviewable configuration changes, while recommending that teams begin by testing one high-volume task class against a cheaper model candidate.

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