Speeding up AI Coding Assistants using Deterministic Feedback

Frequent developer interruptions, including AI-generated errors that require review or correction, can reduce deep-focus time and impose substantial productivity costs, described here through Mean Time Between Interruptions (MTBI). The text argues that current AI coding assistants rely too heavily on static code and documentation, which can lead to hallucinated APIs, inaccurate performance assumptions, and failures across downstream services, increasing the need for human intervention. It presents Proxymock as a production-traffic replay platform that creates sanitized, sandboxed replicas of real system behavior, allowing AI agents and CI pipelines to run functional, contract, fuzz, stress, and performance tests without accessing live systems. By returning deterministic feedback such as failure diffs, metrics, and traces, the platform is intended to let AI systems identify and repair issues before escalating them to developers. The text claims pilot results showed fewer context-switching requests, longer uninterrupted work periods, reclaimed engineering time, and improved code-review acceptance, while emphasizing enterprise features including Kubernetes deployment, data masking, language independence, and integrations with AI development tools.

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