AI Signal 93
Building Reproducible AI Evaluation Workflows with Docker Sandboxes
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An open-source Docker Sandboxes Mixin Kit called the SBX AI Evaluation Kit executes configured commands consistently and preserves runtime evidence to make AI evaluation workflows reproducible across environments.
AI evaluation reproducibility depends on the execution environment, not just the prompt, model, and scoring method, yet most tooling focuses only on what to measure. This kit separates evaluation definitions from execution environments through an executor abstraction, recording structured artifacts that capture what actually ran rather than what was intended.
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The SBX AI Evaluation Kit does not execute AI models or derive evaluation judgments; it executes configured commands and records executor, command, stdout, stderr, exit code, and execution time as structured JSON artifacts.
An executor abstraction allows switching between local and SBX execution by changing only the executor configuration, keeping evaluation definitions independent of where they run.
Evaluation suites group multiple evaluation definitions into a single repeatable workflow, with each evaluation producing its own structured artifact and the suite generating an aggregated summary.
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