Getting started with architecture as code

The basics of architecture-as-code

Architecture-as-code aims to define system architecture as a collection of software systems, containers, components and code. These boundaries are drawn at various levels of abstraction, outlining relationships, dependencies, data flows and deployment topologies. This is done in a structured, machine-readable format, enabling these definitions to be validated, visualized and integrated into CI/CD pipelines.

The C4 model is an example standard that aims to standardize software architecture diagramming with tooling in place to help generate diagrams automatically and keep them up to date. There are several key abstractions recognized in the model, including:

Along with architectural definitions and visualizations, an additional level of detail is included in the form of relationships, dependencies and data flow. Rather than drawing diagrams manually, these relationships can be defined programmatically. Tools can then infer and generate diagrams, validate dependencies and even run governance checks. For example, an architecture is defined using languages like DSL or YAML and committed to a version control system. The build tooling in the CI/CD pipeline will then automatically generate model diagrams and documentation. These can be further optionally linked to IaC templates to ensure coherence between design and deployment.

This approach is useful for distributed microservices, serverless and API-driven architectures, where the landscape changes rapidly and it is difficult to keep manually-created documentation up to date.

Benefits of architecture-as-code

For organizations operating complex distributed systems, architecture-as-code delivers a range of tangible advantages.

Automation and integration

AaC integration with DevOps toolchains can be useful. Architectural models can be validated in CI/CD pipelines, ensuring that system dependencies and API contracts remain intact as code evolves and deployment is close to definition.

Single source of truth

Since architecture is defined as code and stored in GitHub, the documentation remains up-to-date. Any change in architecture -- such as introducing a new service or modifying a dependency -- automatically updates the generated diagrams.

Consistent design and governance

AaC enables organizations to embed fitness functions, which are automated checks that enforce architectural rules, such as dependency direction, data residency and API usage boundaries. This ensures consistent conformance to best practices in architecture.

Enhanced collaboration

Developers, architects and DevOps engineers can co-author architecture definitions using the same workflows they use for code reviews. This brings architectural decision-making closer to day-to-day engineering practice.

Simplified maintenance and traceability

Since all changes are managed in version control and evolution is traceable, lineage and incremental changes can be revised and reasoned about retrospectively. This is a critical capability for audits and compliance.

Challenges of architecture-as-code

Like any emerging practice, AaC introduces its own challenges. Teams must navigate a learning curve, manage security risks and choose wisely among overlapping tools. It also demands a cultural shift and disciplined upkeep to prevent architectural drift. All of this also assumes that existing tooling and practices are mature enough to implement AaC. Let's look at some of the key challenges associated with adopting this paradigm.

Assumed baseline maturity

These tools are useful only if certain practices and paradigms, such as CI/CD and IaaC, are already established on the team. In the absence of those practices, the realizable benefits of AaC are limited.

Learning curve

Teams need to become familiar with new DSLs, YAML templates and frameworks, such as Structurizr or Python-based diagramming tools.

Security risks

Architecture repositories might contain sensitive information about internal systems, APIs or infrastructure topology. Proper access control and repository governance are essential.

Tool fragmentation

The ecosystem is still evolving, with multiple tools offering overlapping features. Choosing the right one for the organization's maturity to ensure use cases are non-trivial. It is also important to make choices that lock the team into a specific ecosystem and to ensure the data remains within the organization.

Cultural shift

Architects and designers accustomed to visual tools might find it challenging to transition to code-first approaches. Organizational buy-in is critical for success.

Maintenance overhead

While AaC reduces manual diagram updates, it introduces the need for maintaining model definitions. Without discipline, these can drift or become stale -- much like neglected documentation. However, unlike static documentation, it might be possible to flag outdated model definitions preemptively. Emerging generative AI tools can help in this regard.

Terraform, AWS CDK and Pulumi are additional AaC tool considerations. Even though these are primarily IaC tools, they complement AaC by implementing the deployment side of the model. When combined, they provide an end-to-end link between logical architecture and physical infrastructure.

Architecture-as-code best practices

Transitioning to architecture-as-code requires a mix of process and cultural enablement. Here are a few distilled best practices that can expedite the journey and make it meaningful for software delivery teams.

• Socialize the goal and purpose. It is helpful to discuss why this approach is useful from the start. This approach fosters wider buy-in and ensures that tools and techniques consistently yield maximum returns for the team.
• Start small and evolve. Begin by modeling one bounded context or subsystem. Capture high-level elements first -- such as the system context and major APIs -- before diving into component-level details.
• Adopt a standard framework. Use the C4 model or a similar standard to maintain consistent abstractions and terminology across teams. The C4 hierarchy -- Context > Container > Component > Code -- aligns naturally with modern distributed systems.
• Integrate into CI/CD pipelines. Treat architecture like any other code artifact. Automate validation, diagram generation and documentation publishing as part of the build and deployment processes.
• Leverage ADRs to manage decisions as code. Maintain ADRs in the repository. This helps teams understand why a decision was made, not just what the architecture looks like.
• Balance automation and human readability. Models should be concise and accessible. YAML templates or Python scripts should be self-explanatory, enabling architects to review them without requiring deep tool expertise.

Generative AI's role in architecture-as-code

One of the key hurdles in adopting AaC is translating unstructured knowledge from design discussions, documents and tacit understanding into a structured, machine-readable form. This is where new generative AI tools are making a visible impact.

It is possible to analyze source code, deployment manifests and chat with transcripts directly to extract architectural intent, identify system boundaries, data flows and dependencies, and express them as C4 model scaffolds or YAML-based templates. These AI-generated drafts provide architects with a strong baseline to refine, validate and enhance. The tooling is expected to become increasingly reliable, serving as a bridge between human design, thinking and structured automation, and enabling architecture-as-code to evolve faster, remain accurate for longer, and mirror real-world systems with greater precision.

Priyank Gupta is a polyglot technologist who is well versed with the craft of building distributed systems that operate at scale. He is an active open source contributor and speaker who loves to solve a difficult business challenge using technology at scale.