Cloud Architecture Anti-Pattern Detection with AI
Detect the cloud anti-patterns your team keeps repeating (shared databases, synchronous chatty services, single points of failure) before they hit production.
Neo4j
AWS
Azure
GCP
TerraformThe problem today
Your team has documented the anti-patterns you want to avoid: chatty synchronous service calls, shared RDS instances across bounded contexts, single-AZ stateful resources, untagged prod. The document is on Confluence. Nobody reads it during design. Six months later, code review catches the problem and it's already in production under a stack of other work.
How AI agents solve it
The Architecture Reviewer uses the Network Digital Map's topology graph as its eyes. It looks for known anti-patterns across the real, live architecture, not on slides. Shared databases are detected by counting distinct service owners on a single RDS instance. Chatty service calls are detected from actual request graphs. Single points of failure are detected by AZ and region distribution analysis. Findings include the specific resource and the refactoring path.
Who this is for: Principal engineers and architects tracking tech debt in mature cloud platforms
Manual workflow vs. Architecture Reviewer
Manual workflow
- Anti-pattern document lives on Confluence, unread
- Code review catches patterns only after they're in prod
- No counting, so nobody knows if we're getting better or worse
- Refactoring prioritization is based on whoever complains loudest
- Same anti-patterns keep reappearing as team rotates
With the Architecture Reviewer
- Every anti-pattern detected against the live architecture
- Violations scored by blast radius and refactoring cost
- Anti-pattern counts trended over time as a tech-debt metric
- PR-time detection means new violations are blocked, not just old ones cataloged
- Refactoring prioritized by evidence, not volume
How the Architecture Reviewer runs this
- 01
Architecture Reviewer loads the anti-pattern catalog (configurable per org)
- 02
Map Agent provides the live topology and service dependency graph
- 03
For each anti-pattern, query the graph for violating resource clusters
- 04
Score each violation by blast radius and refactoring cost
- 05
Generate a per-violation report with evidence and a refactoring path
- 06
Track anti-pattern counts over time as a tech-debt metric
- 07
Integrate with PR review so new violations are caught at merge time
Measurable impact
Reduces anti-pattern introduction rate via PR-time blocking
Makes tech debt measurable with per-pattern trend lines
Refactoring prioritized by objective blast radius, not politics
Anti-pattern knowledge survives team rotation
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.