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Building Reproducible AI Evaluation Workflows with Docker Sandboxes
AI evaluation has never been easier to start. Reproducing it reliably is another story. Developers now have access to more benchmarks, evaluation libraries, model APIs, and agent frameworks than ever before. But keeping the prompt, model, and scoring method fixed doesn’t necessarily make a run reproducible. The execution environment matters too.
Python dependencies change. Local tools drift. Setup steps go undocumented. A workflow that succeeds on one machine may behave differently on another. Most discussions about evaluation focus on what should be measured: benchmarks, scoring methods, or judge models. Much less attention is given to how those evaluations are executed. Yet that execution layer often determines whether someone else can reproduce the same workflow weeks or months later.
When I started exploring Docker Sandboxes, I wasn’t trying to build another evaluation framework. I had a much smaller question.
Could Docker Sandboxes and an SBX Kit make evaluation workflows easier to rerun, inspect, and compare?
That question eventually became the SBX AI Evaluation Kit, an open-source Docker Sandboxes Mixin Kit focused on repeatable execution, structured evaluation records, and runtime evidence. The current implementation does not execute AI models or automatically derive evaluation judgments. Instead, it executes configured commands consistently and preserves evidence of what actually ran.
In practice, the workflow starts by choosing where the evaluation command should run through the execution block:
With executor: sbx, the runner delegates command execution to Docker Sandboxes and writes the runtime evidence into the resulting artifact.
The repository is also packaged as an SBX Mixin Kit, so it can be applied when starting a Claude sandbox:
The runner reads the configured executor and delegates the command to SBX, which executes it inside the sandbox:
From Documentation to an Executable Workflow
Each evaluation is defined in a YAML file that describes the evaluation and the command to run. The repository validates that definition, executes it, and produces a structured JSON record of the result. The difference is in what gets recorded. A written evaluation captures what someone intended to do. An execution-backed evaluation captures what actually happened.
AI evaluation has never been easier to start. Reproducing it reliably is another story. Developers now have access to more benchmarks, evaluation libraries, model APIs, and agent frameworks than ever before. But keeping the prompt, model, and scoring method fixed doesn’t necessarily make a run reproducible. The execution environment matters too.
Python dependencies change. Local tools drift. Setup steps go undocumented. A workflow that succeeds on one machine may behave differently on another. Most discussions about evaluation focus on what should be measured: benchmarks, scoring methods, or judge models. Much less attention is given to how those evaluations are executed. Yet that execution layer often determines whether someone else can reproduce the same workflow weeks or months later.
When I started exploring Docker Sandboxes, I wasn’t trying to build another evaluation framework. I had a much smaller question.
Could Docker Sandboxes and an SBX Kit make evaluation workflows easier to rerun, inspect, and compare?
That question eventually became the SBX AI Evaluation Kit, an open-source Docker Sandboxes Mixin Kit focused on repeatable execution, structured evaluation records, and runtime evidence. The current implementation does not execute AI models or automatically derive evaluation judgments. Instead, it executes configured commands consistently and preserves evidence of what actually ran.
In practice, the workflow starts by choosing where the evaluation command should run through the execution block:
With executor: sbx, the runner delegates command execution to Docker Sandboxes and writes the runtime evidence into the resulting artifact.
The repository is also packaged as an SBX Mixin Kit, so it can be applied when starting a Claude sandbox:
The runner reads the configured executor and delegates the command to SBX, which executes it inside the sandbox:
From Documentation to an Executable Workflow
Each evaluation is defined in a YAML file that describes the evaluation and the command to run. The repository validates that definition, executes it, and produces a structured JSON record of the result. The difference is in what gets recorded. A written evaluation captures what someone intended to do. An execution-backed evaluation captures what actually happened.