Scaling AI Safety to Millions of Requests

As your agent fleet grows from prototype to production, safety infrastructure must scale accordingly. A safety system that cannot keep up with request volume becomes either a bottleneck or gets bypassed. Neither outcome is acceptable.

Scaling the Policy Engine

Authensor's policy engine is pure computation with no I/O. It scales linearly with CPU cores. A single instance handles thousands of evaluations per second. For higher throughput, run multiple instances behind a load balancer.

The engine is stateless. Every evaluation is independent. This means horizontal scaling requires no coordination, shared state, or distributed locking. Add pods and they immediately start handling traffic.

Scaling Content Scanning

Aegis content scanning is the most resource-intensive component. Regex-based scanning scales with the policy engine. ML-based scanning requires separate scaling because it is GPU-bound or requires external API calls.

For ML scanning at scale, use a dedicated scanning service with its own autoscaler. Queue requests through an in-memory buffer and process them in batches. Batch inference is significantly more efficient than individual requests for most ML models.

Set a timeout on ML scanning calls. If the scanner is overloaded, fall back to regex-only scanning rather than blocking the request. Log the fallback for monitoring.

Scaling the Audit Trail

The audit receipt database is the write-heavy component. At millions of requests per day, receipt writes can saturate a single PostgreSQL instance.

Partitioning by time is the simplest approach. Create daily or weekly partitions for the receipts table. Old partitions can be moved to cold storage.

Write buffering collects receipts in memory and flushes them in batches every few seconds. This reduces database round trips by an order of magnitude.

Separate read and write paths. Policy evaluation reads policies. Receipt creation writes audit records. Put these on separate database connections or separate replicas.

Monitoring at Scale

At high throughput, you cannot inspect every safety decision. Focus monitoring on aggregates: block rates, latency percentiles, false positive rates, and error rates. Authensor's Sentinel engine computes these metrics in a streaming fashion without storing every event.

Set alerts on rate changes rather than absolute values. A sudden increase in block rate likely indicates either an attack or a policy misconfiguration.