AI-Powered Terraform Module Review
Every Terraform module reviewed for best practices, security, composability, and versioning discipline, before it lands in your registry.
Terraform
GitHubOPAThe problem today
Your Terraform module registry has 40 modules. Fifteen are great. Ten are 'fine'. Fifteen are from whoever needed them in a hurry three years ago, with no variable descriptions, no sane defaults, hard-coded regions, and version pinning that never got updated. Consumers pick whichever module appears first in search and inherit the debt. Nobody has time to re-review the registry.
How AI agents solve it
The Architecture Reviewer runs a module-quality assessment on every Terraform module in the registry. It checks: variable documentation completeness, default-value sanity, region/account neutrality, resource naming conventions, output completeness, version pinning discipline, and security defaults (encryption, logging, IAM least-privilege). The Security Agent adds policy checks. Each module gets a quality score and a punch list of fixes.
Who this is for: Platform teams maintaining an internal Terraform module registry
Manual workflow vs. Architecture Reviewer
Manual workflow
- Module registry is a grab bag; quality varies wildly
- Consumers pick modules by search order, inheriting debt
- No periodic re-review of registry modules
- Quality issues only surface when a consumer complains
- Module authors have no feedback loop on quality
With the Architecture Reviewer
- Every module continuously scored on a shared rubric
- Quality scores visible in the registry to guide choice
- High-impact fixes opened as PRs automatically
- Module authors get actionable, specific feedback
- Registry quality trends visible at the platform level
How the Architecture Reviewer runs this
- 01
Architecture Reviewer crawls every module in the registry
- 02
Evaluate each module against the quality rubric
- 03
Security Agent runs OPA and security-default checks
- 04
Score modules on a shared rubric: documentation, defaults, security, versioning
- 05
Generate a punch list of improvements per module
- 06
Display quality scores in the registry UI to guide consumer choice
- 07
Open fix PRs for the highest-impact issues automatically
Measurable impact
Lifts registry-wide module quality through continuous scoring
Consumers pick higher-quality modules by default
Module authors get feedback loops that improve their work
Reduces module-quality-related support load on platform teams
Agents involved
Governed by the AI Gateway
Every agent action in this use case is audited, policy-checked, and cost-tracked
Structura's AI Gateway sits between every agent and the underlying LLM providers. Every decision made during this use case. Every plan review, every policy check, every fix PR, is routed through guardrails, logged to an immutable audit trail, and evaluated against NIST AI RMF and AIUC-1 controls.
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See this use case in a live demo
We'll walk you through exactly how the Architecture Reviewer handles this in a real environment with your stack, your policies, and your constraints.