Add /api/ai/intelligence/anomalies endpoint that compares live metrics
against learned baselines to surface deviations - all deterministic
(no LLM required).
Backend:
- Add AnomalyReport struct with severity classification
- Add CheckResourceAnomalies method to baseline store
- Add HandleGetAnomalies API handler
- Add GetStateProvider getter to AI service
Frontend:
- Add AnomalyReport and AnomaliesResponse types
- Add getAnomalies API function
- Add AnomalySeverity type
This is the first step toward surfacing deterministic intelligence
directly in the UI without requiring LLM interaction.
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.
