Most platform teams already have the data needed for better architecture decisions: incident timelines, noisy endpoints, retry storms, and cost spikes. The gap is usually not data collection but a consistent review loop that turns evidence into a clear engineering decision.
In our .NET services, we run an architecture review every sprint for high-churn components. AI is used as an analysis assistant, not as an approver: it summarizes incident clusters and proposes trade-offs, while the owning engineer keeps final accountability for design and rollout.
A practical loop that stays lightweight
The pattern below keeps review cost low and gives enough structure for repeatable decisions across teams.
- Record one ADR per decision candidate
- Attach telemetry evidence: latency percentile, error budget burn, and queue depth
- Ask AI to summarize options with explicit trade-offs
- Define one measurable rollout hypothesis before implementation
- Review after release and close the ADR with observed outcome
public async Task<DecisionSummary> ReviewAsync(
ArchitectureDecision adr,
IncidentSnapshot snapshot,
CancellationToken ct)
{
var prompt = PromptBuilder.ForDecision(adr, snapshot);
var result = await _aiClient.EvaluateAsync(prompt, ct);
return DecisionSummary
.From(result)
.WithSloHypothesis(snapshot.ErrorBudget);
}Treat AI output as advisory evidence. Ownership for architecture decisions and rollout safety remains with the engineering team.
This approach keeps AI useful without governance theater. You get faster synthesis, clearer trade-offs, and decisions that can be audited after production traffic confirms or disproves the hypothesis.