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“Everything as code” means managing repeatable parts of engineering systems—such as infrastructure, configuration, policies, and operational documentation—with software delivery practices: version control, review, testing, and controlled deployment. It is an approach, not a single product or a rule that every human decision should be programmed.
What “everything as code” means
Amazon Web Services describes everything as code as applying version control, testing, and deployment practices across development lifecycle work, including networking infrastructure, documentation, and configuration. The practical idea is to make consequential, repeatable changes visible and manageable as maintained definitions rather than relying on undocumented manual steps.
There is no universally agreed, exhaustive list of what belongs under the umbrella. AWS’s indicators include infrastructure, network modernization, data operations, continuous configuration, technical and operational documentation, generated infrastructure as code (IaC), and compute image distribution. These are related applications of a principle, not parts of one formal standard.
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What teams can manage as code
Infrastructure as code
IaC describes infrastructure in version-controlled files and uses tooling to create or update resources. HashiCorp characterizes IaC as declarative configuration: the definitions specify a desired state that can be reviewed, tested, and deployed using practices also used for application code.
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Policy as code
Policy as code represents governance rules in machine-readable form, keeps them under version control, and tests and validates them. Microsoft recommends integrating policy validation into relevant application or infrastructure CI/CD workflows so teams can find out how a policy behaves before production. HashiCorp describes policy code as a way to version, test, and automate policy logic, providing guardrails for automated systems.
Configuration, documentation, data, and images
Configuration as code makes application or system settings controlled and repeatable; continuous configuration extends that approach to ongoing management. Documentation as code brings technical and operational documentation into the development lifecycle so it can be maintained alongside system changes. Teams can also codify data operations, network changes, and the generation and distribution of compute images.
How to implement it safely
- Choose repeatable, consequential work. Start with infrastructure or policies that people currently recreate or change manually. Keep the first scope small enough for reviewers to understand.
- Put definitions in version control. The UK Home Office Engineering Guidance and Standards says infrastructure definitions should be stored in a source-code repository and treated like application code.
- Make changes reviewable. Use manageable changes, clear history, and pull requests or an equivalent review process. The Home Office standard recommends branching, pull-request processes, versioning, and tags.
- Validate before deployment. At minimum, check syntax. Add security scanning and dry runs where available. The Home Office recommends validating early, such as on a feature-branch commit; Microsoft recommends putting policy validation in relevant CI/CD workflows before deployment.
- Deploy through a pipeline. The Home Office recommends a continuous deployment pipeline and discourages routine changes through cloud consoles or command-line tools. Teams should define how emergency exceptions are authorized; afterward, reconcile emergency changes with the source of truth to reduce drift.
- Keep secrets out of definitions. Do not commit passwords, tokens, or private keys in IaC files. The Home Office warns that people who can read the code could use embedded credentials to impersonate systems, and recommends an appropriate secrets-management tool.
- Check declared state against deployed state. Investigate unexplained manual edits or policy mutations as possible configuration drift. Microsoft notes that policy effects that silently modify deployed settings can cause the code’s declared state and deployed configuration to diverge.
How to choose an approach
There is no vendor ranking established by the available guidance. A useful choice depends on whether people can understand and review the definitions, validate changes locally and in CI/CD, manage secrets and policy checks, and see when deployed systems no longer match their declared state.
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| Approach | How it works | What to evaluate |
|---|---|---|
| Declarative desired-state definitions | People describe the intended state; tooling applies changes to bring managed resources toward it. | Whether the desired state is readable and reviewable, how changes are validated, and how drift is surfaced. |
| IaC generated from a general-purpose language | Code written in a general-purpose language produces infrastructure definitions. | Whether reviewers can trace the generated result, what testing and security checks are available, and how the approach fits existing CI/CD and platform workflows. |
Both approaches need a clear review and validation path. Policy guardrails can help constrain automation, but teams must understand and test policy behavior in the deployment context; policy languages and systems vary.
What the approach enables—and what it does not
A well-run process can provide traceable change history, peer review, repeatable environments, automated checks, clearer recovery procedures, and a closer connection between documented intent and deployed resources. These are capabilities the process can enable, not guaranteed outcomes: putting a setting in a repository does not make it safe, reliable, inexpensive, or faster to deliver.
Automation can also distribute a defective definition quickly. Review, testing, policy checks, and access controls remain necessary. Policy guardrails become more valuable as automation expands, but manual verification can be too slow to keep pace with automated systems, according to HashiCorp’s Sentinel documentation. That makes tested, well-understood controls important—not simply more controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What one study says about IaC failure patterns
A 2020 study by Akond Rahman, Effat Farhana, and Laurie Williams quantitatively analyzed 2,138 open-source IaC scripts across 94 repositories and surveyed 51 practitioners. The authors identified five development anti-patterns associated with defective IaC scripts: “boss is not around,” “many cooks spoil,” “minors are spoiler,” “silos,” and “unfocused contribution.” These are findings from that study, not a complete taxonomy of current IaC failures; the 51 survey respondents are not a representative estimate of all engineering teams.
The paper also recounts a Wikimedia Commons incident in which a defective script erased home directories for approximately 270 users. That account is secondary within the paper, so the figure should be understood as the paper’s report rather than an independently verified incident record here.
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How to think about configuration drift
Drift occurs when deployed settings no longer match the state represented in the source of truth. A manual console change may be a legitimate emergency action, but if it is not brought back into the managed definitions, later deployments can preserve or overwrite a state nobody intended. Similarly, a policy that silently mutates deployed settings can make the live environment differ from its declared configuration.
Teams can limit this gap by restricting routine out-of-band changes, making exceptions explicit, and comparing deployed state with the definitions and policies that are supposed to govern it. The exact detection and reconciliation mechanisms depend on the infrastructure and policy tooling in use.
Bottom line
Everything as code is most useful when a team applies software-engineering discipline to repeatable system changes: define the intended state, review it, validate it, deploy it through a controlled workflow, and check that the running system still matches. The code is a source of traceability and automation—not a substitute for sound decisions, secure secret handling, or operational oversight.
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