Architecture as Code

Most software architecture is recorded for people. Teams draw system diagrams, write Architecture Decision Records, document standards, and explain trade-offs in design reviews. Those artifacts are valuable, but the implementation cannot usually ask them a precise question. A build cannot reliably determine whether a new dependency crosses a forbidden boundary by reading a diagram. An AI coding agent cannot infer which sentence in a six-page ADR is binding and which sentence records historical context.

Architecture as Code changes the medium. It expresses selected parts of architectural intent in a form that tools can evaluate. The result may be a textual architecture model, an architecture description language, a dependency rule, a fitness function, a schema, or a set of assertions. What matters is not the filename or syntax. What matters is that architecture becomes addressable by software.

This definition is intentionally broader than “diagrams as code.” Generating a diagram from text is useful because the source can be reviewed and versioned. Architecture as Code goes further when the same source can answer questions, validate relationships, measure qualities, or produce feedback about whether implementation still matches intent.

What becomes code?

Architecture is not a single artifact. Different tools make different parts of it executable:

• Structure: systems, containers, components, interfaces, and their allowed relationships.
• Constraints: dependency direction, approved technologies, data boundaries, and ownership rules.
• Quality attributes: measurable expectations for latency, resiliency, security, modifiability, or cost.
• Governance: rules about what changes require review, which exceptions are permitted, and where evidence must be recorded.
• Communication: generated views and reports that keep multiple stakeholders aligned around the same underlying model.

Neal Ford and Mark Richards describe Architecture as Code as defining architecture in executable form and using fitness functions plus automated feedback across implementation, infrastructure, teams, governance, and security. Their framing is important because it treats code as a communication and feedback medium, not merely a test suite.

Architecture as Code vs Architecture Decision Records

Architecture Decision Records preserve why a decision was made: the context, alternatives, trade-offs, and consequences. Architecture as Code can express what the resulting architecture contains or must satisfy. Neither replaces the other.

Consider an ADR that chooses a shared HTTP client so retries, timeouts, and tracing remain centralized. The ADR explains the decision and gives it authority. An executable rule can state that service modules may not import a direct HTTP library. The rule is easier to evaluate; the ADR is easier to understand and revisit. The strongest system links the verdict back to the decision that justifies it.

Architecture as Code vs Infrastructure as Code

Infrastructure as Code describes and provisions resources such as networks, clusters, databases, and access configuration. It turns an intended infrastructure state into repeatable deployment operations.

Architecture as Code has a wider subject. It can describe the logical and physical architecture, the relationships between components, the qualities the system must preserve, and the constraints that apply across code, infrastructure, and organization. Infrastructure code can be one input to architectural verification, but provisioning a compliant cluster does not prove that the application respects its intended module boundaries.

Architecture as Code vs Policy as Code

The concepts overlap when an architectural constraint becomes a policy decision. A model might state that only adapters may depend on a legacy subsystem. A policy evaluator can decide whether a proposed edit violates that relationship. The model supplies architectural meaning; the policy supplies executable decision logic; an enforcement point gives the decision practical authority.

ADLs and architectural fitness functions

Architecture description languages

An architecture description language, or ADL, provides a formal or semi-formal notation for describing systems, components, connectors, and configuration rules. ADLs make architecture precise enough for automated analysis while keeping it above the level of implementation detail. Textual models such as FINOS CALM illustrate the contemporary Architecture as Code direction: human-readable source, machine validation, and shared vocabulary.

Mneme does not need to become another general-purpose ADL. ADLs, ADRs, and other architecture-as-code formats can sit upstream. Their structured output can become input to the narrower job of scoping and enforcing decisions during software change.

Architectural fitness functions

An architectural fitness function is an objective assessment of an architectural characteristic. It can be a test, metric, monitor, or other automated check. Fitness functions turn qualities such as dependency direction, response time, coupling, or resiliency into repeatable feedback.

They are one of the most useful mechanisms inside Architecture as Code because they close part of the distance between architectural intent and implementation. They also reveal the next boundary: a check that reports failure is not automatically a control that prevents the incompatible action.

Architecture as Code for AI coding agents

Architecture as Code becomes more important when the primary implementer is an AI coding agent. Agents can generate plausible local changes faster than teams can restate system-wide context. Human-readable architecture helps, but machine-readable architecture gives the agent harness a stable surface it can retrieve, evaluate, and cite.

For an agentic workflow, a useful architecture-as-code artifact needs more than syntax. It needs:

• Scope: the system must know which rules apply to the files, components, or services the agent intends to change.
• Timing: the relevant intent must reach the agent before it commits to an incompatible implementation.
• Determinism: the same proposed change and the same applicable rule should produce the same verdict.
• Authority: a blocking rule must be connected to an enforcement point that can stop or redirect the action.
• Evidence: the verdict should identify the source decision, rule, scope, and enforcement event.

These requirements connect Architecture as Code to executable architectural intent, governance before generation, and enforcement provenance. The architecture artifact is the beginning of the control chain, not the entire chain.

Architecture as Code is not necessarily enforcement

This distinction matters. An assertion, fitness function, or architecture specification can detect that something is wrong. That does not necessarily prevent an AI coding agent from making the incompatible change. The result may arrive after generation, after a commit, or during CI. It may be advisory. It may have no authority to block anything.

The gap is not a criticism of Architecture as Code. Feedback remains valuable, and not every architectural property should be enforced as a hard gate. It is a distinction about timing and authority. Teams need to decide which architecture-as-code checks are informative, which are merge gates, and which must become deterministic controls at the agent action boundary.

Where Mneme fits

Mneme occupies the enforcement path downstream of architectural intent. It turns selected decisions into scoped rules, supplies the relevant constraints to agent workflows, evaluates proposed changes, and records why a verdict occurred. ADRs and architecture-as-code formats remain the sources of authority; Mneme makes their binding subset operational where AI agents edit software.

That makes Mneme an enforcement layer for Architecture as Code, not a replacement for architecture modeling, ADLs, ADRs, or fitness functions. The category connection is simple: architecture becomes machine-readable upstream, and the rules that must bind agents become enforceable downstream.

Sources and further reading

• Neal Ford and Mark Richards, Architecture as Code: executable architecture, fitness functions, continuous validation, and feedback across organizational intersections.
• Building Evolutionary Architectures, sample chapter: architectural fitness functions as objective assessments that guide architectural evolution.
• FINOS Common Architecture Language Model: a current example of human-readable, machine-validated software architecture as code.