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Everything as code means managing repeatable parts of building and operating systems—such as infrastructure, configuration, policies, and documentation—with software delivery practices like version control, review, testing, and deployment. It is an engineering approach, not a single product or a requirement to program every human decision. Its value depends on how carefully teams define, validate, and operate the code.
What does everything as code mean?
Amazon Web Services describes the practice as applying version control, testing, and deployment to areas of the development lifecycle that include networking infrastructure, documentation, and configuration. The central idea is to make important, repeatable changes visible and manageable as reviewed definitions rather than relying on undocumented, one-off actions.
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The phrase is an umbrella, not a universally agreed checklist or formal standard. It brings related practices together: define system changes in a maintainable form, keep their history, check them before release, and deliver them through a controlled process. It does not mean every judgment, conversation, or exception can—or should—be automated.
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What can a team manage as code?
Start with work that is repeatable, consequential, and likely to benefit from a clear change history. AWS’s indicators for the practice include infrastructure, networking, data operations, continuous configuration, technical and operational documentation, generated infrastructure definitions, and compute image distribution.
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Infrastructure as code
Infrastructure as code (IaC) describes resources and their desired state in version-controlled files, then uses tooling to apply those definitions. HashiCorp characterizes IaC as declarative configuration that can be reviewed, tested, and deployed using practices familiar from application development. For example, instead of creating the same environment manually each time, a team can maintain a reusable definition of its resources.
Policy as code
Policy as code puts machine-readable governance rules under source control so they can be reviewed, tested, and validated. These rules can act as guardrails for automated systems. Microsoft recommends incorporating policy validation into relevant application or infrastructure CI/CD workflows, allowing teams to discover how a policy behaves before deployment. HashiCorp likewise describes versioning, testing, and automating policy logic as part of this approach.
Configuration, documentation, and operations
Configuration as code manages application or system settings in a repeatable, controlled form. Documentation as code brings technical and operational documentation into the same development lifecycle as the systems it describes, making changes easier to maintain alongside related system changes.
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The umbrella can also cover codified data operations, network modernization through IaC, and automated creation and distribution of compute images. These practices are related, but they do not have to use the same language, tool, or pipeline.
How to implement it safely
The UK Home Office Engineering Guidance and Standards recommends treating infrastructure definitions like application code. Its guidance, last updated 3 August 2023, supports a practical workflow: keep definitions in a repository, review changes, validate early, and deploy through a pipeline.
- Choose a small, repeatable starting point. Identify infrastructure or policy work that people currently recreate or change manually. Keep the first scope narrow enough that reviewers can understand the proposed change and its likely effects.
- Put the definition in source control. Store the files that describe the intended infrastructure, configuration, or policy in a source-code repository. Give changes a clear history so the team can see what changed and when.
- Make review part of the change. Use manageable changes and a branch-and-review process, such as a pull request or an equivalent approval workflow. The Home Office standard also recommends versioning and tags. Review should check both the technical diff and the operational consequences—not simply whether the file parses.
- Validate before deployment. Run syntax checks as a baseline, then add appropriate automated tests, security scanning, policy checks, or dry runs. The Home Office guidance recommends validating early, such as when a feature branch is committed. For policy changes, Microsoft recommends validation inside the relevant CI/CD workflow before deployment.
- Deploy through a controlled pipeline. Use a continuous deployment pipeline for routine infrastructure changes rather than making them directly through a cloud console or command-line tool. Define who can approve and release changes, and what checks must pass before the pipeline applies them.
- Keep credentials out of definition files. Do not commit passwords, tokens, or private keys in IaC. The Home Office standard warns that someone who can read the code could use embedded credentials to impersonate systems. Use an appropriate secrets-management tool and limit access to secrets.
- Compare declared and deployed state. Check whether live settings still match the definitions and policies in source control. Investigate unexplained manual edits or policy-driven changes as possible configuration drift, then decide how the source of truth should be updated.
Teams may need emergency changes outside the normal pipeline; whether and how to permit them is a governance decision. As an operational safeguard, document such changes and reconcile them back into the source of truth. Otherwise, the next automated deployment may reintroduce the old state or overwrite the emergency fix. Reconciliation is a practical implementation recommendation, rather than a direct requirement quoted from the cited standards.
How to choose an approach or tool
There is no evidence here for ranking vendors. Compare approaches against the work your team needs to control, and test them in the deployment context where they will be used.
| Decision area | What to evaluate |
|---|---|
| Definition style | Whether a declarative desired-state definition fits the work, or whether generating infrastructure definitions from a general-purpose language is more suitable. AWS and HashiCorp describe relevant practices, but neither approach is universally best. |
| Reviewability | Whether engineers can readily understand the definitions and review their effects before approval. |
| Validation | Which local checks and automated tests are available, and whether they can run early in the workflow. |
| Security and governance | How the workflow supports security scanning, secret-management integration, and policy guardrails. |
| Delivery fit | Whether it works with the team’s existing CI/CD and cloud or platform workflows. |
| Drift and exceptions | How the system reveals differences between declared and deployed state, and how out-of-band or emergency changes return to the managed definitions. |
Policy languages and systems vary. A policy that appears sensible in isolation can behave differently in a particular deployment workflow, so test its effects where it will actually run rather than assuming that a successful syntax check proves the intended outcome.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What benefits can the practice enable?
A well-designed process can make changes traceable, give peers a chance to review them, support repeatable environments, automate checks, and clarify recovery procedures. Keeping documentation in the lifecycle can also make it easier to align written intent with deployed resources. These are capabilities the process can enable, not guaranteed business results: the cited guidance does not establish that adopting the label alone reliably improves speed, security, cost, or availability.
Automation also increases the reach of mistakes. A defective definition or unsafe policy can be reviewed and deployed just as systematically as a sound one. Version control is not a substitute for testing, access controls, careful review, or security checks.
What research says about IaC failure patterns
A 2020 study by Akond Rahman, Effat Farhana, and Laurie Williams analyzed 2,138 open-source IaC scripts across 94 repositories and surveyed 51 practitioners. The survey was part of that study; its 51 respondents should not be treated as a representative estimate of all engineering teams. The authors identified five development anti-patterns associated with defective IaC scripts:
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- “Many cooks spoil”
- “Minors are spoiler”
- “Silos”
- “Unfocused contribution”
These are findings from that study, not an exhaustive taxonomy of current IaC failures. The paper also recounts a Wikimedia Commons incident in which a defective script erased home directories for approximately 270 users. That account is reported secondhand in the paper, so it should not be treated as an independently verified incident record.
What everything as code does not guarantee
- It does not make every task suitable for automation or every human decision reducible to a rule.
- It does not make infrastructure secure or reliable merely because its definitions are stored in a repository.
- It does not eliminate configuration drift; live changes and policy effects can cause deployed settings to diverge from declared code.
- It does not make a pipeline safe by default. Review, validation, access control, and careful handling of secrets still matter.
The useful test is not whether a team has put everything in a repository. It is whether the definitions cover the repeatable work that matters, changes can be understood and checked before release, and the deployed system can be reconciled with the intended state.
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