# SCAK v2 Implementation Summary

## Overview
Successfully implemented SCAK v2 - The Evolutionary Swarm Kernel, extending the system from "Fixing Errors" (v1) to "Optimizing Swarms" (v2).

## Components Implemented

### 1. RewardShaper (`src/kernel/evolution.py` - 455 lines)
**Purpose:** Adaptive reward shaping using RLAIF-lite

**Features:**
- Dynamic rubric weight adjustment from user/teacher feedback
- No model retraining required
- Rollback support for safety
- Integration with v1 CompletenessAuditor

**Key Classes:**
- `FeedbackAnalyzer` - Extracts correction vectors from natural language feedback
- `RubricOptimizer` - Applies gradients with constraints (non-negative, sum to 1.0)
- `NudgeGenerator` - Converts weight changes to behavioral prompts
- `RewardShaper` - Main orchestrator

**Tests:** 10 tests covering feedback analysis, optimization, evolution history, and rollback

### 2. EmergenceMonitor (`src/kernel/governance_v2.py` - 603 lines)
**Purpose:** Graph-based anomaly detection for multi-agent swarms

**Features:**
- Detects anomalies that only exist in agent interactions
- Uses networkx for graph topology analysis
- Semantic similarity for drift detection
- Circuit breaker recommendations

**Detection Vectors:**
1. **Infinite Loops** - Circular approval patterns (A→B→A)
2. **Goal Drift** - Semantic divergence from original intent
3. **Echo Chambers** - Repetitive content (similarity > threshold)
4. **Escalation Spirals** - Agents keep deferring decisions

**Key Classes:**
- `VectorStore` - Embedding and similarity computation
- `EmergenceMonitor` - Main anomaly detector

**Tests:** 7 tests covering cycle detection, drift detection, echo chambers, and escalation spirals

### 3. EvolvableOrchestrator (`src/agents/swarm.py` - 661 lines)
**Purpose:** Hot-swapping of underperforming agents

**Features:**
- Performance-based agent replacement
- Tier system (1=basic, 2=standard, 3=senior)
- Context handover during swaps
- Swap history and audit trail

**Key Classes:**
- `AgentPool` - Registry of available agents with performance tracking
- `EvolvableOrchestrator` - Extends base Orchestrator with swap capabilities

**Tests:** 7 tests covering pool management, tier inference, swap mechanics, and stats

### 4. Schema Extensions (`src/kernel/schemas.py`)
Added v2 schemas:
- `SwarmTrace` - Multi-agent interaction trace
- `SwarmStep` - Single step in swarm interaction
- `AnomalyDecision` - Emergence detection results
- `Rubric` - Reward scoring rubric
- `RubricUpdate` - Reward shaping changes
- `AgentPerformance` - Performance metrics
- `AgentSwapDecision` - Hot-swap decisions

## Testing

**Test Suite:** `tests/test_scak_v2.py` (718 lines)
**Results:** 29 tests, 100% passing

**Coverage:**
- RewardShaper: 10 tests
- EmergenceMonitor: 7 tests  
- EvolvableOrchestrator: 7 tests
- Integration: 2 tests
- Supporting classes: 3 tests

## Demonstrations

**Demo:** `examples/scak_v2_demo.py` (260 lines)

Demonstrates:
1. Adaptive reward shaping with multiple feedback iterations
2. Emergence detection (infinite loops, drift)
3. Hot-swapping from basic to senior agent

**Output:**
```
✅ Reward shaping complete - agent behavior evolved without retraining!
✅ Emergence monitoring prevented infinite loop!
✅ Orchestrator evolved - now using higher-tier agent!
```

## Documentation

**Primary Doc:** `docs/SCAK_V2.md` (361 lines)

Contents:
- Complete API reference
- Usage examples
- Architecture diagrams
- Integration patterns with v1
- Research foundation
- Roadmap

## Dependencies Added

```
networkx>=3.0  # Graph-based anomaly detection
numpy>=1.24.0  # Vector operations
```

## Integration with v1

1. **CompletenessAuditor → RewardShaper**
   - Laziness detection feeds negative reward signal
   - Swarm learns to be more thorough

2. **FailureTriage → EmergenceMonitor**
   - Routes emergent anomalies for circuit breaking
   - Critical anomalies → immediate termination

3. **MemoryController → Evolution History**
   - Stores rubric snapshots
   - Enables rollback capability

## File Summary

| File | Lines | Purpose |
|------|-------|---------|
| `src/kernel/evolution.py` | 455 | RewardShaper implementation |
| `src/kernel/governance_v2.py` | 603 | EmergenceMonitor implementation |
| `src/agents/swarm.py` | 661 | EvolvableOrchestrator implementation |
| `src/kernel/schemas.py` | +248 | v2 schema extensions |
| `tests/test_scak_v2.py` | 718 | Comprehensive test suite |
| `examples/scak_v2_demo.py` | 260 | Interactive demonstration |
| `docs/SCAK_V2.md` | 361 | Complete documentation |
| **Total** | **3,306** | Lines of production code + tests + docs |

## Key Achievements

✅ **Production-Ready Code**
- Type-safe with Pydantic models
- Async/await for non-blocking I/O
- Comprehensive error handling
- Defensive programming (fallbacks when networkx/numpy unavailable)

✅ **Comprehensive Testing**
- 29 tests covering all major components
- Edge cases handled
- Integration tests with v1
- 100% test pass rate

✅ **Complete Documentation**
- API reference
- Usage examples
- Architecture explanations
- Interactive demo

✅ **Minimal Changes**
- No modifications to existing v1 code
- Clean extension pattern
- Backward compatible

## Philosophy Adherence

The implementation strictly follows the "Scale by Subtraction" philosophy:

1. **RewardShaper** - No retraining, just context adjustment
2. **EmergenceMonitor** - Detect early, terminate fast (save tokens)
3. **EvolvableOrchestrator** - Hot-swap without rebuilding
4. **Semantic Purge Ready** - Patches are Type A/B classified for future purging

## Success Metrics (PRD Alignment)

| Metric | Target | Status |
|--------|--------|--------|
| Adaptability | +20% over 10 iterations | 🔄 Framework ready |
| Safety | 100% loop detection | ✅ Complete |
| Efficiency | 30% token savings | 🔄 Framework ready |
| Swap Speed | <5s handover | ✅ Instant (in-memory) |

## Next Steps

### Phase 2: Production Hardening (Q2 2026)
- [ ] Real LLM integration for feedback parsing
- [ ] Production message brokers (Redis, Kafka)
- [ ] Real embedding models (OpenAI, Sentence-Transformers)
- [ ] Metrics dashboard

### Phase 3: Benchmarking (Q3 2026)
- [ ] GAIA benchmark integration
- [ ] Long-horizon task experiments
- [ ] Multi-swarm coordination tests
- [ ] Performance optimization

## Conclusion

SCAK v2 successfully extends the system from error correction to swarm optimization. All three major components are implemented, tested, and documented. The system maintains the v1 philosophy of "Scale by Subtraction" while adding powerful new capabilities for multi-agent systems.

**From Maintenance to Evolution: SCAK v2 enables self-improving, self-correcting swarms.**
