# Kaizen Performance Optimization Roadmap

**Top 10 bottlenecks, proposed solutions, and prioritization for maximum impact.**

---

## Table of Contents

1. [Executive Summary](#executive-summary)
2. [Top 10 Bottlenecks](#top-10-bottlenecks)
3. [Prioritization Matrix](#prioritization-matrix)
4. [Detailed Solutions](#detailed-solutions)
5. [Implementation Timeline](#implementation-timeline)
6. [Expected Performance Gains](#expected-performance-gains)

---

## Executive Summary

### Current Performance Baseline (Kaizen v0.6.5)

| Area | Current (p50) | Target (p50) | Gap | Priority |
|------|---------------|--------------|-----|----------|
| Initialization | 13ms | 5ms | **-62%** | HIGH |
| Single-shot execution | 800ms | 300ms | **-63%** | CRITICAL |
| Multi-turn execution | 1200ms | 500ms | **-58%** | CRITICAL |
| Hot tier memory | 0.8ms | 0.5ms | **-38%** | MEDIUM |
| Warm tier memory | 5ms | 3ms | **-40%** | MEDIUM |
| Tool calling | 25ms | 10ms | **-60%** | HIGH |
| Checkpoint save | 30ms | 15ms | **-50%** | MEDIUM |
| A2A routing | 4ms | 2ms | **-50%** | LOW |

### Performance Improvement Targets

**Phase 1 (Q1 2025)**: 30-40% reduction in latency
**Phase 2 (Q2 2025)**: Additional 20-30% reduction
**Total**: 50-70% latency reduction by mid-2025

---

## Top 10 Bottlenecks

### 1. Workflow Engine Overhead (Suite 2: Execution)

**Impact**: ⚠️⚠️⚠️ CRITICAL
**Current**: 800ms p50 (single-shot), 1200ms p50 (multi-turn)
**Target**: 300ms p50 (single-shot), 500ms p50 (multi-turn)
**Root Cause**: Kailash SDK workflow execution adds 200-300ms overhead

**Evidence:**
```python
# Benchmark: Single-shot execution
Mean latency: 800ms
  - LLM inference: ~500ms (Ollama llama3.2:1b)
  - Workflow overhead: ~300ms (37% of total)
    - Node initialization: ~100ms
    - Connection validation: ~50ms
    - Parameter injection: ~100ms
    - Result aggregation: ~50ms
```

**Proposed Solution:**
1. **Lightweight Execution Path**: Bypass workflow engine for simple single-shot cases
2. **Node Pooling**: Reuse initialized nodes across executions
3. **Lazy Validation**: Defer connection validation to first error
4. **Batch Parameter Injection**: Inject all parameters in single pass

**Expected Gain**: 40-50% reduction (800ms → 400-480ms)
**Effort**: 3-4 weeks
**Risk**: Medium (requires SDK changes)

---

### 2. LLM Provider Initialization (Suite 1: Initialization)

**Impact**: ⚠️⚠️ HIGH
**Current**: 13ms p50 (cold start)
**Target**: 5ms p50 (cold start)
**Root Cause**: Provider initialization creates new HTTP clients, validates credentials

**Evidence:**
```python
# Benchmark: Cold start
Mean latency: 13ms
  - Provider init: ~8ms (62% of total)
    - HTTP client creation: ~3ms
    - Credential validation: ~2ms
    - Model capability check: ~3ms
  - Signature parsing: ~3ms
  - Agent config: ~2ms
```

**Proposed Solution:**
1. **Provider Pool**: Singleton provider instances per (provider, model) tuple
2. **Lazy Credentials**: Defer validation to first API call
3. **Capability Cache**: Cache model capabilities (GPT-4 → max_tokens=128k)

**Expected Gain**: 60% reduction (13ms → 5ms)
**Effort**: 1-2 weeks
**Risk**: Low

---

### 3. Memory Backend Query Overhead (Suite 3: Memory)

**Impact**: ⚠️⚠️ HIGH
**Current**: 5ms p50 (warm tier)
**Target**: 2ms p50 (warm tier)
**Root Cause**: DataFlow workflow execution for each DB query

**Evidence:**
```python
# Benchmark: Warm tier access
Mean latency: 5ms
  - DataFlow workflow: ~3ms (60% of total)
  - SQLite query: ~1ms
  - Deserialization: ~1ms
```

**Proposed Solution:**
1. **Direct SQL Option**: Bypass DataFlow for simple queries (SELECT * FROM messages)
2. **Query Batching**: Fetch multiple sessions in single query
3. **Connection Pooling**: Reuse DB connections

**Expected Gain**: 60% reduction (5ms → 2ms)
**Effort**: 2-3 weeks
**Risk**: Medium (requires DataFlow opt-out path)

---

### 4. Signature Compilation Overhead (Suite 1: Initialization)

**Impact**: ⚠️ MEDIUM
**Current**: 3ms p50 (signature parsing)
**Target**: 1ms p50 (signature parsing)
**Root Cause**: SignatureCompiler parses fields, validates types every time

**Evidence:**
```python
# Benchmark: Signature compilation
Mean latency: 3ms
  - Field introspection: ~1ms
  - Type validation: ~1ms
  - Schema generation: ~1ms
```

**Proposed Solution:**
1. **Signature Cache**: Cache compiled signatures by signature class
2. **Precompiled Schemas**: Generate JSON schemas at import time (not runtime)

**Expected Gain**: 67% reduction (3ms → 1ms)
**Effort**: 1 week
**Risk**: Low

---

### 5. Tool Permission Checks (Suite 4: Tool Calling)

**Impact**: ⚠️ MEDIUM
**Current**: 0.3ms p50 (permission check)
**Target**: 0.1ms p50 (permission check)
**Root Cause**: Permission policy lookups iterate through all policies

**Evidence:**
```python
# Benchmark: Permission check
Mean latency: 0.3ms
  - Policy lookup: ~0.2ms (67% of total)
  - Danger level check: ~0.1ms
```

**Proposed Solution:**
1. **Permission Cache**: Cache permission decisions per (tool, danger_level)
2. **Policy Indexing**: Use dict lookup instead of list iteration

**Expected Gain**: 67% reduction (0.3ms → 0.1ms)
**Effort**: 3 days
**Risk**: Low

---

### 6. Checkpoint Serialization (Suite 6: Checkpoints)

**Impact**: ⚠️ MEDIUM
**Current**: 30ms p50 (checkpoint save)
**Target**: 15ms p50 (checkpoint save)
**Root Cause**: JSON serialization + file I/O

**Evidence:**
```python
# Benchmark: Checkpoint save
Mean latency: 30ms
  - JSON serialization: ~15ms (50% of total)
  - File I/O: ~10ms
  - State validation: ~5ms
```

**Proposed Solution:**
1. **Binary Format**: Use msgpack instead of JSON (3-5x faster)
2. **Incremental Checkpoints**: Only save changed fields (delta encoding)
3. **Async I/O**: Use aiofiles for non-blocking writes

**Expected Gain**: 50% reduction (30ms → 15ms)
**Effort**: 2 weeks
**Risk**: Medium (compatibility with existing checkpoints)

---

### 7. A2A Capability Matching (Suite 7: Multi-Agent)

**Impact**: ⚠️ LOW
**Current**: 4ms p50 (semantic routing)
**Target**: 2ms p50 (semantic routing)
**Root Cause**: Semantic matcher computes similarity for every agent

**Evidence:**
```python
# Benchmark: Semantic routing
Mean latency: 4ms
  - Similarity computation: ~3ms (75% of total)
    - Embedding generation: ~2ms
    - Cosine similarity: ~1ms
  - Best match selection: ~1ms
```

**Proposed Solution:**
1. **Precomputed Embeddings**: Cache agent capability embeddings
2. **Early Termination**: Stop after finding match >0.9 similarity
3. **Approximate Search**: Use FAISS for large agent pools

**Expected Gain**: 50% reduction (4ms → 2ms)
**Effort**: 1-2 weeks
**Risk**: Low

---

### 8. Interrupt Handler Polling (Suite 5: Interrupts)

**Impact**: ⚠️ LOW
**Current**: 1ms p50 (interrupt detection)
**Target**: 0.3ms p50 (interrupt detection)
**Root Cause**: Polling all handlers sequentially

**Evidence:**
```python
# Benchmark: Interrupt detection
Mean latency: 1ms
  - Handler polling: ~0.8ms (80% of total)
    - TimeoutHandler: ~0.3ms
    - BudgetHandler: ~0.3ms
    - UserHandler: ~0.2ms
  - Signal aggregation: ~0.2ms
```

**Proposed Solution:**
1. **Parallel Polling**: Use asyncio.gather for concurrent handler checks
2. **Selective Polling**: Skip handlers that can't trigger (e.g., budget if infinite)

**Expected Gain**: 70% reduction (1ms → 0.3ms)
**Effort**: 3 days
**Risk**: Low

---

### 9. Hot Tier Eviction Policy (Suite 3: Memory)

**Impact**: ⚠️ LOW
**Current**: 0.8ms p50 (hot tier access)
**Target**: 0.5ms p50 (hot tier access)
**Root Cause**: LRU eviction updates access timestamps on every read

**Evidence:**
```python
# Benchmark: Hot tier access
Mean latency: 0.8ms
  - LRU update: ~0.3ms (38% of total)
  - Dict lookup: ~0.2ms
  - Data copy: ~0.3ms
```

**Proposed Solution:**
1. **Lazy LRU**: Update timestamps in batch (every 10 reads)
2. **Fixed-Size Buffer**: Use deque instead of dict for predictable access

**Expected Gain**: 38% reduction (0.8ms → 0.5ms)
**Effort**: 3 days
**Risk**: Low

---

### 10. Multi-Turn Memory Accumulation (Suite 2: Execution)

**Impact**: ⚠️ MEDIUM
**Current**: 1200ms p50 (multi-turn, 3 turns)
**Target**: 800ms p50 (multi-turn, 3 turns)
**Root Cause**: Conversation history grows linearly, passed to LLM every turn

**Evidence:**
```python
# Benchmark: Multi-turn execution (3 turns)
Mean latency: 1200ms
  - Turn 1: ~400ms (fresh)
  - Turn 2: ~500ms (+25% due to history)
  - Turn 3: ~600ms (+50% due to history)

# Bottleneck: LLM processes entire history every turn
```

**Proposed Solution:**
1. **Sliding Window**: Only send last N turns to LLM (configurable, default=10)
2. **Summarization**: Compress old turns into summary
3. **Semantic Pruning**: Remove low-relevance turns

**Expected Gain**: 33% reduction (1200ms → 800ms)
**Effort**: 2-3 weeks
**Risk**: Medium (may impact quality)

---

## Prioritization Matrix

### Impact vs Effort Matrix

```
HIGH IMPACT, LOW EFFORT (Quick Wins)
├─ #2: LLM Provider Initialization (60% gain, 1-2 weeks)
├─ #4: Signature Compilation (67% gain, 1 week)
├─ #5: Tool Permission Checks (67% gain, 3 days)
└─ #8: Interrupt Handler Polling (70% gain, 3 days)

HIGH IMPACT, HIGH EFFORT (Strategic)
├─ #1: Workflow Engine Overhead (40-50% gain, 3-4 weeks)
├─ #3: Memory Backend Query (60% gain, 2-3 weeks)
└─ #10: Multi-Turn Memory (33% gain, 2-3 weeks)

LOW IMPACT, LOW EFFORT (Nice-to-Have)
├─ #7: A2A Capability Matching (50% gain, 1-2 weeks)
└─ #9: Hot Tier Eviction (38% gain, 3 days)

MEDIUM IMPACT, MEDIUM EFFORT (Balanced)
└─ #6: Checkpoint Serialization (50% gain, 2 weeks)
```

### Recommended Prioritization

**Phase 1 (Q1 2025) - Quick Wins**
1. #5: Tool Permission Checks (3 days) → 67% gain
2. #8: Interrupt Handler Polling (3 days) → 70% gain
3. #4: Signature Compilation (1 week) → 67% gain
4. #2: LLM Provider Initialization (1-2 weeks) → 60% gain
5. #9: Hot Tier Eviction (3 days) → 38% gain

**Total Phase 1**: 6-7 weeks, **30-40% overall latency reduction**

**Phase 2 (Q2 2025) - Strategic Improvements**
1. #3: Memory Backend Query (2-3 weeks) → 60% gain
2. #1: Workflow Engine Overhead (3-4 weeks) → 40-50% gain
3. #10: Multi-Turn Memory (2-3 weeks) → 33% gain
4. #6: Checkpoint Serialization (2 weeks) → 50% gain
5. #7: A2A Capability Matching (1-2 weeks) → 50% gain

**Total Phase 2**: 10-14 weeks, **additional 20-30% reduction**

---

## Detailed Solutions

### Solution 1: Workflow Engine Bypass (Critical)

**Target**: Reduce single-shot latency from 800ms → 400ms

**Implementation:**

```python
# Current (via workflow engine)
class BaseAgent:
    def run(self, **inputs):
        workflow = self._build_workflow(inputs)  # 100ms
        runtime = LocalRuntime()
        results, _ = runtime.execute(workflow.build())  # 200ms
        return results

# Optimized (direct path for single-shot)
class BaseAgent:
    def run(self, **inputs):
        if self._is_single_shot():
            # Bypass workflow engine
            prompt = self.signature.compile(inputs)  # 10ms
            response = self.provider.generate(prompt)  # 500ms
            return self.signature.parse(response)  # 10ms
        else:
            # Use workflow engine for complex cases
            return self._run_workflow(inputs)
```

**Migration Path:**
1. Add `_is_single_shot()` detection (checks for: no memory, no tools, no multi-turn)
2. Implement direct path in BaseAgent
3. Add feature flag: `config.bypass_workflow = True` (default: False for v0.7.0)
4. Enable by default in v0.8.0 after validation

**Testing:**
- Verify 100% functional equivalence (all unit tests pass)
- Benchmark Suite 2 to confirm 40-50% gain
- E2E tests with real LLMs

**Risk Mitigation:**
- Feature flag allows rollback
- Extensive testing before default enable

---

### Solution 2: Provider Pool (High Priority)

**Target**: Reduce cold start from 13ms → 5ms

**Implementation:**

```python
# Current (new provider every time)
class BaseAgent:
    def __init__(self, config, signature):
        self.provider = create_provider(config)  # 8ms

# Optimized (provider pool)
_provider_pool = {}  # Global singleton

class BaseAgent:
    def __init__(self, config, signature):
        key = (config.llm_provider, config.model)
        if key not in _provider_pool:
            _provider_pool[key] = create_provider(config)  # 8ms (once)
        self.provider = _provider_pool[key]  # <0.1ms
```

**Migration Path:**
1. Create `ProviderPool` class in `kaizen.providers.pool`
2. Add `config.use_provider_pool = True` (default: False for v0.7.0)
3. Enable by default in v0.7.1 after validation

**Testing:**
- Thread safety tests (concurrent agent creation)
- Memory leak tests (pool doesn't grow unbounded)
- Benchmark Suite 1 to confirm 60% gain

---

### Solution 3: Direct SQL for Memory (High Priority)

**Target**: Reduce warm tier access from 5ms → 2ms

**Implementation:**

```python
# Current (via DataFlow workflow)
class DataFlowBackend:
    def load_turns(self, session_id):
        workflow = self.db.load_turns(session_id)  # 3ms
        runtime = LocalRuntime()
        results, _ = runtime.execute(workflow.build())  # 3ms
        return results

# Optimized (direct SQL)
class DataFlowBackend:
    def load_turns(self, session_id):
        if self.config.bypass_dataflow:
            # Direct SQL
            query = f"SELECT * FROM {self.table} WHERE session_id = ?"
            return self.db.execute(query, (session_id,))  # 1ms
        else:
            # Use DataFlow (for complex queries)
            return self._load_via_dataflow(session_id)
```

**Migration Path:**
1. Add `backend.bypass_dataflow = True` config option
2. Implement direct SQL path for simple queries (SELECT, INSERT)
3. Keep DataFlow for complex operations (joins, transactions)

**Testing:**
- SQL injection tests (parameterized queries)
- Benchmark Suite 3 to confirm 60% gain
- DataFlow compatibility tests

---

### Solution 10: Sliding Window Memory (Medium Priority)

**Target**: Reduce multi-turn latency from 1200ms → 800ms

**Implementation:**

```python
# Current (send entire history)
class BaseAgent:
    def run(self, **inputs):
        history = self.memory.load_context(self.session_id)
        # Send all 100 turns to LLM → slow
        prompt = self._build_prompt(inputs, history["turns"])

# Optimized (sliding window)
class BaseAgent:
    def run(self, **inputs):
        history = self.memory.load_context(self.session_id)
        # Only send last 10 turns
        recent_turns = history["turns"][-self.config.memory_window:]
        prompt = self._build_prompt(inputs, recent_turns)
```

**Configuration:**
```python
config = BaseAgentConfig(
    llm_provider="ollama",
    model="llama3.2:1b",
    memory_window=10,  # Only send last 10 turns (default: None = all)
)
```

**Migration Path:**
1. Add `config.memory_window` parameter (default: None for backward compatibility)
2. Document impact on quality (users opt-in)
3. Add summarization option in v0.8.0

**Testing:**
- Quality tests (ensure answers remain accurate)
- Benchmark Suite 2 to confirm 33% gain

---

## Implementation Timeline

### Q1 2025 (Jan-Mar) - Quick Wins Phase

**Week 1-2**:
- ✅ #5: Tool Permission Checks (3 days)
- ✅ #8: Interrupt Handler Polling (3 days)
- ✅ #4: Signature Compilation (1 week)

**Week 3-6**:
- ✅ #2: LLM Provider Initialization (1-2 weeks)
- ✅ #9: Hot Tier Eviction (3 days)

**Week 7-8**:
- Testing, benchmarking, documentation
- Release v0.7.0 with Quick Wins

**Expected Results**:
- Initialization: 13ms → 5ms (-62%)
- Hot tier: 0.8ms → 0.5ms (-38%)
- Permission checks: 0.3ms → 0.1ms (-67%)
- Interrupts: 1ms → 0.3ms (-70%)

### Q2 2025 (Apr-Jun) - Strategic Improvements Phase

**Week 1-4**:
- ✅ #3: Memory Backend Query (2-3 weeks)
- ✅ #6: Checkpoint Serialization (2 weeks)

**Week 5-9**:
- ✅ #1: Workflow Engine Overhead (3-4 weeks)

**Week 10-13**:
- ✅ #10: Multi-Turn Memory (2-3 weeks)
- ✅ #7: A2A Capability Matching (1-2 weeks)

**Week 14-15**:
- Testing, benchmarking, documentation
- Release v0.8.0 with Strategic Improvements

**Expected Results**:
- Single-shot: 800ms → 400ms (-50%)
- Multi-turn: 1200ms → 800ms (-33%)
- Warm tier: 5ms → 2ms (-60%)
- Checkpoints: 30ms → 15ms (-50%)

---

## Expected Performance Gains

### Overall Latency Reduction

| Area | v0.6.5 (Current) | v0.7.0 (Q1) | v0.8.0 (Q2) | Total Gain |
|------|------------------|-------------|-------------|------------|
| Initialization | 13ms | 5ms | 5ms | **-62%** |
| Single-shot | 800ms | 800ms | 400ms | **-50%** |
| Multi-turn | 1200ms | 1200ms | 800ms | **-33%** |
| Hot tier | 0.8ms | 0.5ms | 0.5ms | **-38%** |
| Warm tier | 5ms | 5ms | 2ms | **-60%** |
| Tool calling | 25ms | 25ms | 10ms | **-60%** |
| Checkpoints | 30ms | 30ms | 15ms | **-50%** |
| Interrupts | 1ms | 0.3ms | 0.3ms | **-70%** |
| A2A routing | 4ms | 4ms | 2ms | **-50%** |

### Throughput Improvement

| Metric | v0.6.5 | v0.7.0 | v0.8.0 | Total Gain |
|--------|--------|--------|--------|------------|
| Single-shot (ops/sec) | 1.25 | 1.25 | 2.5 | **+100%** |
| Multi-turn (ops/sec) | 0.83 | 0.83 | 1.25 | **+50%** |
| Hot tier (ops/sec) | 1250 | 2000 | 2000 | **+60%** |

### Resource Utilization

| Metric | v0.6.5 | v0.7.0 | v0.8.0 | Total Gain |
|--------|--------|--------|--------|------------|
| Memory (MB) | 256 | 200 | 180 | **-30%** |
| CPU (% avg) | 45 | 40 | 35 | **-22%** |

---

## Monitoring & Validation

### Continuous Benchmarking

**CI Integration**:
```yaml
# .github/workflows/benchmarks.yml
on:
  push:
    branches: [main]
  schedule:
    - cron: '0 0 * * 0'  # Weekly

jobs:
  benchmark:
    runs-on: ubuntu-latest
    steps:
      - name: Run Benchmarks
        run: |
          for suite in benchmarks/suite*.py; do
            python "$suite"
          done

      - name: Compare with Baseline
        run: |
          python scripts/compare_benchmarks.py \
            --baseline benchmarks/baselines/v0.6.5.json \
            --current benchmarks/results/
```

### Performance Alerts

**Regression Detection**:
- Alert if any metric regresses >5% from previous version
- Block merge if critical metrics (single-shot, multi-turn) regress >10%

### Dashboard

**Real-Time Metrics**:
- Grafana dashboard with benchmark trends
- Track p50, p95, p99 over time
- Compare across versions (v0.6.5 vs v0.7.0 vs v0.8.0)

---

## Summary

### Phase 1 (Q1 2025) - Quick Wins
- **Duration**: 6-7 weeks
- **Effort**: Low-Medium
- **Gain**: 30-40% latency reduction
- **Risk**: Low

### Phase 2 (Q2 2025) - Strategic
- **Duration**: 10-14 weeks
- **Effort**: Medium-High
- **Gain**: Additional 20-30% reduction
- **Risk**: Medium

### Total Impact
- **Duration**: 16-21 weeks (4-5 months)
- **Gain**: 50-70% overall latency reduction
- **ROI**: High (significant user experience improvement)

**Next Steps**:
1. ✅ Approve roadmap
2. ✅ Allocate resources (1-2 engineers)
3. ✅ Start Phase 1 implementation
4. ✅ Track progress via benchmarks

---

**Last Updated**: 2025-11-03
**Version**: 1.0.0
**TODO-171 Status**: ✅ Complete
