AI Agent Memory: How Streaming Databases Power Agent State

Real-time anomaly detection identifies unusual patterns in streaming data — traffic spikes, sensor failures, fraud attempts, system errors — as they happen, not hours later in a batch report. SQL materialized views in a streaming database can implement statistical anomaly detection without ML infrastructure.

SQL-Based Anomaly Detection

-- Calculate rolling statistics
CREATE MATERIALIZED VIEW metric_stats AS
SELECT metric_name,
  AVG(value) as mean_1h,
  STDDEV(value) as stddev_1h,
  COUNT(*) as samples_1h
FROM metrics WHERE ts > NOW() - INTERVAL '1 hour'
GROUP BY metric_name;

-- Detect anomalies (>3 standard deviations)
CREATE MATERIALIZED VIEW anomalies AS
SELECT m.metric_name, m.value, m.ts, s.mean_1h, s.stddev_1h,
  (m.value - s.mean_1h) / NULLIF(s.stddev_1h, 0) as z_score
FROM metrics m JOIN metric_stats s ON m.metric_name = s.metric_name
WHERE ABS((m.value - s.mean_1h) / NULLIF(s.stddev_1h, 0)) > 3;

Anomaly Detection Patterns

Frequently Asked Questions

Can SQL replace ML for anomaly detection?

For statistical anomalies (z-score, rate changes, missing data), SQL is sufficient and simpler. For complex, learned patterns (behavioral anomalies, sophisticated fraud), combine SQL features with ML models via UDFs.