BoxProbe

API behavior drift, tracked over time

Scout itself does not include a scheduler, alerting, long-term storage, or a monitoring UI. The monitoring use case is an application pattern — scheduled scout runs plus an analysis layer on top — that produces drift timelines, schema-change views, and endpoint inventories that single-shot release testing can't.

Scheduled runs + portal-side analysis

Single-shot testing (the testing use case) answers "what changed between v1 and v2?". Monitoring answers different questions: "what's the trend over the last six months?", "when did this endpoint first appear?", "which release introduced this schema change?".

01

Scheduler

Cron, GitHub Actions on a schedule, or any periodic trigger. Runs scout run scenarios/ against your reference environments at the cadence you choose (per release, nightly, weekly).

02

Run artifact store

Each scout run produces a deterministic recording. The pattern stores these artifacts in object storage (S3 / R2 / etc.) so they accumulate as historical record.

03

Analysis layer

Pulls accumulated runs, computes pairwise diffs, builds timelines and inventories, exposes them via a UI or pushes alerts on configured thresholds. Where the value beyond raw scout output gets created.

From accumulated scout runs
The public Medusa regression lab

cases/medusa is a live instance of this pattern, operated by BoxProbe. Periodically: pin a new Medusa release, run scout against the previous pair + the new pair, publish each comparison as a page under /cases/medusa/<version-comparison>. The project hub aggregates them. Eventually multiple comparisons form a drift timeline; today there's one because we're at v1.

See the Medusa lab
Stack for this pattern

From scout (free)

  • Deterministic scenario runs that produce comparable artifacts
  • HTML diff report and JUnit XML per comparison
  • SQLite record per run (queryable for analysis)

You assemble (or hire us)

  • Scheduler (cron / GHA on schedule / Temporal / ...)
  • Object storage for run artifacts (S3 / R2 / GCS)
  • Analysis layer (your stack: Python / TS / SQL)
  • Dashboard / API / alerting (your UI of choice)
  • Maintained scenario suite as the app evolves

BoxProbe offers a managed version of this pattern as a service — scenario authoring, periodic runs, hosted analytical reports — for teams that want monitoring without building the assembly themselves. See /services for the "Public regression lab" and "Continuous coverage" offerings.

Boundaries

Want a managed regression lab?

BoxProbe runs this pattern as a service: we author scenarios, schedule runs, and publish the analytical reports on a hosted project hub. Email us with a URL and a release cadence.

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