Continuous Architecture Assessment with AI Agents
Architecture health scored every day across every workload, with drill-downs into reliability, cost, and complexity trends.
Terraform
Neo4j
AWS
Azure
GCPThe problem today
Architecture reviews are calendar events. You do them before a launch, during an audit, or after an incident. In between, architecture health is a vibe. Nobody can answer 'is our architecture getting better or worse?' with data. When someone on the leadership team asks, you point at the most recent outage or celebrate the most recent launch.
How AI agents solve it
The Architecture Reviewer runs a continuous assessment every day across every workload, scoring it on reliability, cost efficiency, operational complexity, and change velocity. The scores are trended. Drops are alerted. The Terraform Agent contributes change-velocity signal; the Map Agent contributes complexity signal. Leadership gets a real trend, engineers get directional feedback, and architecture decisions are made against actual data.
Who this is for: CTOs, principal engineers, and platform leaders who need measurable architecture signals
Manual workflow vs. Architecture Reviewer
Manual workflow
- Architecture health is a vibe, not a metric
- Reviews only happen around launches, audits, or incidents
- Leadership questions answered with anecdotes
- No way to know if we're trending up or down
- Architecture decisions made without baseline data
With the Architecture Reviewer
- Architecture health scored daily across every workload
- Trend lines replace vibes for leadership reporting
- Score drops caught before they become incidents
- Decisions backed by multi-month trend data
- Reviews become deep-dives on the trends, not discovery exercises
How the Architecture Reviewer runs this
- 01
Architecture Reviewer defines a scoring rubric per workload type
- 02
Daily assessment run against every workload using live evidence
- 03
Score components: reliability, cost efficiency, complexity, change velocity
- 04
Trend scores over time at workload-level and platform-level
- 05
Alert on any sustained score drop of a configurable magnitude
- 06
Generate a monthly architecture health report for leadership
- 07
Feed score deltas back into PR review as a long-term signal
Measurable impact
Turns 'architecture health' into a measurable, trended metric
Detects architectural regressions weeks before they become incidents
Gives leadership honest trend data instead of anecdotes
Shifts architecture reviews from discovery to deep-dives
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.