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rcourtman
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Phase 2 of Pulse AI differentiation: - Create internal/ai/baseline package for learned baselines - Implement statistical baseline learning with mean, stddev, percentiles - Add z-score based anomaly detection with severity classification (low, medium, high, critical based on standard deviations) - Integrate baseline provider into context builder - Wire baseline store into patrol service with adapters - Add anomaly enrichment to resource contexts Key features: - Learn computes baseline from historical metric data points - IsAnomaly and CheckAnomaly detect deviations from normal - Persists baselines to disk as JSON for durability - Formatted anomaly descriptions for AI consumption Example: 'Memory is high above normal (85.2% vs typical 42.1% ± 8.3%)' The baseline store needs to be initialized and triggered to learn from metrics history. Next step is adding the learning loop. All tests passing.