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4 commits

Author SHA1 Message Date
rcourtman
f98fd845e1 docs: Mark Phase 6 (Multi-Resource Correlation) as complete 
ALL PHASES COMPLETE! 🎉

Pulse AI now has the full 'moat' architecture:

- Phase 1: Historical Context Integration ✅
- Phase 2: Anomaly Detection ✅
- Phase 3: Operational Memory ✅
- Phase 4: Remediation Integration ✅
- Phase 5: Predictive Intelligence ✅
- Phase 6: Multi-Resource Correlation ✅

The AI becomes more valuable the longer Pulse runs by learning:
- Metric trends and baselines
- Infrastructure changes
- Past remediation actions
- Failure patterns
- Resource dependencies
 2025-12-12 14:27:14 +00:00
rcourtman
8b3bfb60d2 docs: Mark Phase 5 (Predictive Intelligence) as complete 
Updated implementation status:
- Phase 1: Historical Context Integration ✅
- Phase 2: Anomaly Detection ✅
- Phase 3: Operational Memory ✅
- Phase 4: Remediation Integration ✅
- Phase 5: Predictive Intelligence ✅ (NEW)
- Phase 6: Multi-Resource Correlation (PLANNED)

Pulse AI now has a complete 'moat' - it becomes more
valuable the longer it runs by learning from:
- Historical metric trends
- Baseline behavior patterns
- Infrastructure changes
- Past remediation actions
- Alert patterns and failure cycles
 2025-12-12 14:16:41 +00:00
rcourtman
dacdd48e28 docs: Update AI architecture doc with implemented phases 
Mark Phases 1-4 as complete:
- Phase 1: Historical Context Integration ✅
- Phase 2: Anomaly Detection ✅
- Phase 3: Operational Memory ✅
- Phase 4: Remediation Integration ✅

Update future phases (5 & 6) with remaining work.

The AI moat is now built: trends, baselines, anomaly detection,
change tracking, and remediation learning are all operational.
 2025-12-12 14:03:50 +00:00
rcourtman
96af101c98 feat(ai): Add enriched context with historical trends and predictions 
Phase 1 of Pulse AI differentiation:

- Create internal/ai/context package with types, trends, builder, formatter
- Implement linear regression for trend computation (growing/declining/stable/volatile)
- Add storage capacity predictions (predicts days until 90% and 100%)
- Wire MetricsHistory from monitor to patrol service
- Update patrol to use buildEnrichedContext instead of basic summary
- Update patrol prompt to reference trend indicators and predictions

This gives the AI awareness of historical patterns, enabling it to:
- Identify resources with concerning growth rates
- Predict capacity exhaustion before it happens
- Distinguish between stable high usage vs growing problems
- Provide more actionable, time-aware insights

All tests passing. Falls back to basic summary if metrics history unavailable.
 2025-12-12 09:45:57 +00:00