Environment Standardization Package. @environment-standardization

Works in staging, fails in production means the environments were never the same system. This package aligns development, test, staging, and production: same topology where it matters, differences made explicit and version-controlled where they are intentional. The finish state is a promotion path where environment-specific failures are rare, and parity is checked automatically rather than assumed.

The sprint audits real differences across environments, configuration, versions, infrastructure, data shape, then normalises configuration into a single declared pattern, aligns provisioning as code, and adds parity checks that catch drift before a deploy finds it.

Environment audit and parity map

Every meaningful difference across environments found and classified: intentional or accidental.

Configuration normalization

One declared configuration pattern, with per-environment values explicit and version-controlled.

Provisioning alignment

Environments built from the same code path, so parity is structural, not manual.

Parity guardrails

Automated checks that flag drift between environments before it becomes an incident.

1. Scope: audit the environments, classify differences, and agree the target parity model.
2. Build: normalise configuration, align provisioning, and implement drift checks.
3. Handover: verify a full promotion cycle, train the team, hand over documentation.

The Environment Standardization Package is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

Do all environments have to be identical?

No. Size and cost can differ deliberately; behaviour-affecting configuration must not differ accidentally. The parity map makes that boundary explicit and the guardrails enforce it.

Does this include test data management?

Data shape is part of the audit; a seeded or masked dataset strategy is included where it drives environment behaviour. Full test-data tooling beyond that is scoped separately.

What builds on this?

Standardized environments make the Preview Environment Setup menu item straightforward, since ephemeral environments are only trustworthy when the base pattern is.