What does an AI architecture audit check?
System boundaries
Where the seams are, whether they follow the domain, and what each component actually owns.
Data ownership
Which component is the source of truth for each entity, and how many others write to it directly.
Coupling
Synchronous call chains, shared databases, and the changes that force a change somewhere else.
Request path
Everything happening inside a request handler that should be behind a queue.
Data model
Schema design, normalisation, constraints, and the indexes the query patterns require.
Query patterns
N+1 access, unbounded result sets, and reads that scale with total rows rather than with the page.
State management
Where state lives, what assumes a single process, and what breaks on a second instance.
Caching
What is cached, what invalidates it, and whether caching is covering for a missing index.
Background work
Queues, retries, idempotency, and what happens to a job that fails on the third attempt.
Integrations
Third-party failure handling, timeouts, and blast radius when a provider is slow rather than down.
Scalability limits
The concurrency the current design carries, and the first component to fail past it.
Failure modes
Single points of failure, cascading timeouts, and what degrades versus what stops.
Environments
Separation between development, staging, and production, and the config that differs between them.
Evolvability
How expensive the likely next three features are given the current structure.
Documentation drift
Where the diagram, the README, and the running system disagree.
Other audits
Each audit answers a different question. Most engagements combine two or three.
Build with
confidence.
AI builds the product. Vibecop makes sure it won’t break in production, fail under scale, or expose your users to risk. One audit. Fewer expensive surprises.