Benchmark coding-agent sandboxes with Polyaxon

A coding-agent sandbox benchmark should answer whether your workflow performs reliably on a particular configuration. A fast empty-container startup does not tell you how long it takes to clone a repository, execute a check, recover work, and deliver a reviewable patch.

Use Polyaxon to organize a repeatable benchmark suite. Compare candidate execution backends through the same application-owned harness rather than through unrelated vendor demos.

Define the unit of useful work

Choose representative tasks before measuring infrastructure. Examples include a small Python fix, a dependency-heavy repository analysis, and a multi-step session that creates and revisits files.

Each fixture should specify the starting commit, input bundle, expected output, allowed dependencies, and completion criterion. A backend that finishes quickly but produces the wrong result should not win.

Keep model behavior constant where possible. To isolate execution infrastructure, replay a fixed command sequence first. Evaluate a live agent separately, because changing tool choices can obscure the infrastructure effect.

Measure the whole path

Record distinct intervals:

• Request accepted to workload scheduled.
• Scheduling to application readiness.
• Input transfer and repository preparation.
• Command execution.
• Output collection and verification.
• Cleanup or checkpoint completion.

Use Polyaxon run timelines for platform lifecycle context and instrument the harness for application-level intervals. Do not subtract timestamps from unsynchronized machines without accounting for clock differences.

Compare distributions and failure rates, not just a single average. Label warm and cold conditions explicitly, including whether images and data were already cached.

Keep the experiment matrix honest

Vary one relevant dimension at a time: backend, resource profile, input size, concurrency, or workspace reuse policy. Record all others as run metadata.

A practical result table includes successful tasks, timeout rate, startup percentiles, command percentiles, bytes transferred, and retained-state correctness. Document the number of repetitions and the observation window.

Avoid claiming a universal winner from one repository or one region. A configuration that performs well for short Python calculations may behave differently for large builds or GPU-dependent tasks.

Include interruption and isolation checks

Exercise authorized scenarios such as a client disconnect, process timeout, and service replacement. Determine whether the harness can recover the task outcome without blindly repeating side effects.

For session reuse, verify that expected files remain within the session and that a new session does not receive another session's data. Use synthetic fixtures, not production secrets.

The sandbox process interface returns exit status, timing, and truncation information. Treat those fields as part of the benchmark result rather than assuming stdout alone establishes success.

Store enough evidence to rerun the decision

Persist the harness revision, component configuration, image reference, input fixture version, and raw measurements through Polyaxon artifacts. Build summaries from those measurements, preserving unsuccessful trials.

Use run comparison to inspect alternatives under equivalent conditions. Keep cost estimates tied to measured resource use and your actual provider terms instead of mixing incompatible public pricing units.

The benchmark becomes a reusable platform asset. When a runtime, image, or scheduling policy changes, rerun the same suite and compare the evidence before changing the default for coding agents.