docs(03-03): complete proactive scaling plan

Tasks completed: 2/2
- Implemented ProactiveScaler class with hybrid monitoring
- Integrated proactive scaling into ModelManager

Proactive scaling system with hybrid monitoring, graceful degradation cascades, and intelligent stabilization periods.

SUMMARY: .planning/phases/03-resource-management/03-03-SUMMARY.md

.planning/STATE.md

@@ -1,7 +1,7 @@
 # Project State & Progress
 
 **Last Updated:** 2026-01-27
-**Current Status:** Phase 3 Plan 2 complete - hardware tier detection implemented
+**Current Status:** Phase 3 Plan 3 complete - proactive scaling with hybrid monitoring implemented
 
 ---
 
@@ -11,7 +11,7 @@
 |--------|-------|
 | **Milestone** | v1.0 Core (Phases 1-5) |
 | **Current Phase** | 03: Resource Management |
-| **Current Plan** | 2 of 4 in current phase |
+| **Current Plan** | 3 of 4 in current phase |
 | **Overall Progress** | 3/15 phases complete |
 | **Progress Bar** | ███████░░░░░ 30% |
 | **Model Profile** | Budget (haiku priority) |
@@ -34,6 +34,14 @@
 - **Discretion**: Claude determines capability tiers, compression algorithms, and degradation specifics
 - **Implementation**: All three plans executed with comprehensive model switching, resource monitoring, and CLI interface
 
+### Phase 3 Complete (Resource Management)
+- **Proactive scaling strategy**: Scale at 80% resource usage for upgrades, 90% for immediate degradation
+- **Hybrid monitoring**: Combined continuous background monitoring with pre-flight checks for comprehensive coverage
+- **Graceful degradation**: Complete current tasks before switching models to maintain user experience
+- **Stabilization periods**: 5-minute cooldowns prevent model switching thrashing during volatile conditions
+- **Performance tracking**: Use actual response times and failure rates for data-driven scaling decisions
+- **Implementation**: ProactiveScaler integrated into ModelManager with seamless scaling callbacks
+
 ---
 
 ## Recent Work
@@ -51,25 +59,26 @@
 - **2026-01-27**: **EXECUTED** Phase 2, Plan 03 - Created tamper-proof audit logging system with SHA-256 hash chains
 - **2026-01-27**: **EXECUTED** Phase 2, Plan 04 - Implemented safety system integration and comprehensive testing
 - **2026-01-27**: Phase 2 complete - sandbox execution environment with security assessment, audit logging, and resource management fully implemented
+- **2026-01-27**: **EXECUTED** Phase 3, Plan 3 - Implemented proactive scaling system with hybrid monitoring and graceful degradation
 
 ---
 
 ## What's Next
 
-Phase 3 Plan 2 complete. Ready for Phase 3 Plan 3: Proactive scaling with hybrid monitoring.
+Phase 3 Plan 3 complete. Ready for Phase 3 Plan 4: Resource optimization algorithms.
 Phase 3 requirements:
 - Detect available system resources (CPU, RAM, GPU) ✓
 - Select appropriate models based on resources ✓
 - Request more resources when bottlenecks detected
 - Graceful scaling from low-end hardware to high-end systems
 
-Status: Phase 3 Plan 2 complete, 2 plans remaining.
+Status: Phase 3 Plan 3 complete, 1 plan remaining.
 
 ---
 
 ## Blockers & Concerns
 
-None — all Phase 3 Plan 2 deliverables complete and verified. Hardware tier detection and classification system implemented with configurable YAML definitions.
+None — all Phase 3 Plan 3 deliverables complete and verified. Proactive scaling system with hybrid monitoring and graceful degradation cascades implemented and integrated.
 
 ---
 
@@ -88,6 +97,6 @@ None — all Phase 3 Plan 2 deliverables complete and verified. Hardware tier de
 
 ## Session Continuity
 
-Last session: 2026-01-27T23:32:51Z
-Stopped at: Completed 03-02-PLAN.md
+Last session: 2026-01-27T23:53:00Z
+Stopped at: Completed 03-03-PLAN.md
 Resume file: None

.planning/phases/03-resource-management/03-03-SUMMARY.md

@@ -0,0 +1,114 @@
+---
+phase: 03-resource-management
+plan: 03
+subsystem: resource-management
+tags: [proactive-scaling, hybrid-monitoring, resource-management, graceful-degradation]
+
+# Dependency graph
+requires:
+  - phase: 03-01
+    provides: Resource monitoring foundation
+  - phase: 03-02
+    provides: Hardware tier detection and classification
+provides:
+  - Proactive scaling system with hybrid monitoring and graceful degradation
+  - Integration between ModelManager and ProactiveScaler
+  - Pre-flight resource checks for model operations
+  - Performance tracking for scaling decisions
+affects: [04-memory-management, 05-conversation-engine]
+
+# Tech tracking
+tech-stack:
+  added: []
+  patterns: [hybrid-monitoring, proactive-scaling, graceful-degradation, stabilization-periods]
+
+key-files:
+  created: [src/resource/scaling.py]
+  modified: [src/models/model_manager.py]
+
+key-decisions:
+  - "Proactive scaling prevents performance degradation before it impacts users"
+  - "Hybrid monitoring combines continuous checks with pre-flight validation"
+  - "Graceful degradation completes current tasks before model switching"
+  - "Stabilization periods prevent model switching thrashing"
+
+patterns-established:
+  - "Pattern 1: Hybrid monitoring with background threads and pre-flight checks"
+  - "Pattern 2: Graceful degradation cascades with immediate and planned switches"
+  - "Pattern 3: Performance trend analysis for predictive scaling decisions"
+  - "Pattern 4: Hysteresis and stabilization periods to prevent thrashing"
+
+# Metrics
+duration: 15min
+completed: 2026-01-27
+---
+
+# Phase 3: Resource Management Summary
+
+**Proactive scaling system with hybrid monitoring, graceful degradation cascades, and intelligent stabilization periods for resource-aware model management**
+
+## Performance
+
+- **Duration:** 15 minutes
+- **Started:** 2026-01-27T23:38:00Z
+- **Completed:** 2026-01-27T23:53:00Z
+- **Tasks:** 2
+- **Files modified:** 2
+
+## Accomplishments
+
+- **Created comprehensive ProactiveScaler class** with hybrid monitoring architecture combining continuous background monitoring with pre-flight checks
+- **Implemented graceful degradation cascades** that complete current tasks before switching to smaller models
+- **Added intelligent stabilization periods** (5 minutes for upgrades) to prevent model switching thrashing
+- **Integrated ProactiveScaler into ModelManager** with seamless scaling callbacks and performance tracking
+- **Enhanced model selection logic** to consider scaling recommendations and resource trends
+- **Implemented performance metrics tracking** for data-driven scaling decisions
+
+## Task Commits
+
+Each task was committed atomically:
+
+1. **Task 1: Implement ProactiveScaler class** - `4d7749d` (feat)
+2. **Task 2: Integrate proactive scaling into ModelManager** - `53b8ef7` (feat)
+
+**Plan metadata:** N/A (will be committed with summary)
+
+## Files Created/Modified
+
+- `src/resource/scaling.py` - Complete ProactiveScaler implementation with hybrid monitoring, trend analysis, and graceful degradation
+- `src/models/model_manager.py` - Enhanced ModelManager with ProactiveScaler integration, pre-flight checks, and performance tracking
+
+## Decisions Made
+
+- **Hybrid monitoring approach**: Combined continuous background monitoring with pre-flight checks for comprehensive resource awareness
+- **Proactive scaling thresholds**: Scale at 80% resource usage for upgrades, 90% for immediate degradation
+- **Stabilization periods**: 5-minute cooldowns prevent model switching thrashing during volatile resource conditions
+- **Graceful degradation**: Complete current tasks before switching models to maintain user experience
+- **Performance-driven scaling**: Use actual response times and failure rates for intelligent scaling decisions
+
+## Deviations from Plan
+
+None - plan executed exactly as written.
+
+## Issues Encountered
+
+None - all implementation completed successfully with full verification passing.
+
+## User Setup Required
+
+None - no external service configuration required.
+
+## Next Phase Readiness
+
+Proactive scaling system is complete and ready for integration with memory management and conversation engine phases. The hybrid monitoring approach provides:
+
+- Resource-aware model selection with tier-based optimization
+- Predictive scaling based on usage trends and performance metrics
+- Graceful degradation that maintains conversation flow during resource constraints
+- Stabilization periods that prevent unnecessary model switching
+
+The system maintains backward compatibility with existing ModelManager functionality while adding intelligent resource management capabilities.
+
+---
+*Phase: 03-resource-management*
+*Completed: 2026-01-27*