Suppose we have an enterprise‑level AI system that discovers database tables and metadata, executes read‑only queries, and audits various items with many concurrent users. How would you architect this system?

A robust architecture would separate concerns into distinct microservices. 1) Metadata Service – a catalog (e.g., Apache Atlas or AWS Glue) that ingests schema information via JDBC/ODBC connectors and stores it in a searchable store (Cassandra or DynamoDB). 2) Query Service – a stateless API that receives read‑only SQL, validates it against a whitelist of allowed statements, and forwards it to a query engine (Presto/Trino or Spark SQL) that can run on a cluster. 3) Audit Service – logs every query, user, and result set to a tamper‑evident store (e.g., S3 with versioning or a write‑once log). 4) Concurrency Layer – use a message queue (Kafka) to serialize heavy queries and a rate‑limiter per user to prevent overload. 5) Security – enforce RBAC via an IAM system, encrypt data at rest and in transit, and use a gateway that performs token validation. 6) Observability – metrics (Prometheus), tracing (OpenTelemetry), and alerting (Grafana) to monitor latency, error rates, and audit trails. 7) Scalability – autoscale the query engine and metadata service based on CPU/memory usage, and use a CDN for static assets. This layered approach keeps the system modular, secure, and able to handle thousands of concurrent users.