# Phase 4 Action Items - Outstanding Work

Prioritized action items to move from current partial completions to full production readiness.

## Priority Levels

- 🔴 **P0 - CRITICAL**: Must complete before production
- 🟠 **P1 - HIGH**: Should complete before production
- 🟡 **P2 - MEDIUM**: Complete in first month of production
- 🟢 **P3 - LOW**: Nice to have, complete over time

---

## P0 - CRITICAL (Before Production)

### 1. Production Overhead Validation ✅ COMPLETE

**Status**: ✅ COMPLETE (2025-10-24)
**Owner**: testing-specialist
**Effort**: 1 day (actual)
**Result**: APPROVED FOR PRODUCTION

**Completed**:
- ✅ 100 requests with real OpenAI API calls (gpt-3.5-turbo)
- ✅ Measured overhead with actual agent workloads (1000-1500ms operations)
- ✅ Documented actual overhead percentages
- ✅ All NFRs exceeded (0% vs. <10% target)
- ✅ Updated PERFORMANCE_RESULTS.md with real data

**Final Results**:
```
Baseline:           1150.62ms average (50 samples)
With Observability: 1149.98ms average (49 samples, 1 outlier removed)
Overhead:           -0.64ms (-0.06%)

Status: ✅ PASS - Well below 10% target
Decision: ✅ APPROVED FOR PRODUCTION
```

**Key Findings**:
- **Negligible overhead**: 0% overhead (within measurement noise)
- **Outlier detection**: IQR method correctly identified network timeout (488s)
- **Real workloads**: Validated against 1000-1500ms LLM operations
- **Statistical significance**: 50-sample test provides high confidence
- **All systems tested**: Metrics + Logging + Tracing + Audit (all enabled)

**Files Updated**:
- ✅ `benchmarks/PRODUCTION_OVERHEAD_RESULTS.md` - Complete validation report
- ✅ `benchmarks/production_overhead_validation.py` - Validation script with outlier detection
- ✅ `docs/observability/IMPLEMENTATION_STATUS.md` - Production validation status

**Cost**: $0.20 (100 requests, well under $5 budget)

---

### 2. Security Hardening 🔴

**Status**: ⏳ Not Started
**Owner**: TBD
**Effort**: 3-5 days
**Blocking**: Production deployment

**Current State**:
- ❌ Default admin password (admin/admin)
- ❌ No TLS/HTTPS
- ❌ No authentication beyond basic auth
- ❌ Secrets in docker-compose.yml

**Required Changes**:

**A. Grafana Security**:
```yaml
# docker-compose.yml
environment:
  - GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_ADMIN_PASSWORD}  # From .env
  - GF_SERVER_PROTOCOL=https
  - GF_SERVER_CERT_FILE=/etc/grafana/ssl/grafana.crt
  - GF_SERVER_CERT_KEY=/etc/grafana/ssl/grafana.key
  - GF_AUTH_OAUTH_ENABLED=true  # Enable OAuth
```

**B. Prometheus Security**:
```yaml
# prometheus.yml
global:
  external_labels:
    environment: 'production'

# Add authentication
basic_auth:
  username: ${PROM_USER}
  password: ${PROM_PASSWORD}
```

**C. Jaeger Security**:
```yaml
# docker-compose.yml
environment:
  - SPAN_STORAGE_TYPE=elasticsearch  # Production storage
  - ES_USERNAME=${ES_USER}
  - ES_PASSWORD=${ES_PASSWORD}
  - COLLECTOR_OTLP_ENABLED=true
  - COLLECTOR_ZIPKIN_HOST_PORT=:9411
```

**D. Secrets Management**:
- [ ] Create `.env.production` for secrets
- [ ] Add `.env.production` to `.gitignore`
- [ ] Document secret rotation procedures

**Success Criteria**:
- [ ] No default passwords
- [ ] HTTPS/TLS enabled for all services
- [ ] OAuth or LDAP authentication configured
- [ ] Secrets in environment variables, not config files
- [ ] Security audit completed
- [ ] Penetration testing passed (if required)

**Files to Create**:
- `grafana/.env.production.example`
- `grafana/ssl/` directory with cert instructions
- `docs/observability/SECURITY.md` - Security guide

**Files to Update**:
- `grafana/docker-compose.yml`
- `grafana/prometheus.yml`
- `grafana/README.md` - Security section

---

### 3. Tier 3 E2E Tests 🔴

**Status**: ⏳ Not Started
**Owner**: TBD
**Effort**: 3-5 days
**Blocking**: Confidence in production deployment

**Current State**:
- ✅ 176/176 Tier 1 & 2 tests passing
- ❌ No Tier 3 E2E tests with real LLM providers

**Required Tests**:

**Test 1: Full Observability with Real OpenAI**:
```python
@pytest.mark.tier3
@pytest.mark.asyncio
async def test_full_observability_with_real_openai():
    """Test complete observability stack with real OpenAI calls."""
    agent = BaseAgent(
        config=BaseAgentConfig(
            llm_provider="openai",
            model="gpt-3.5-turbo",
            api_key=os.getenv("OPENAI_API_KEY")
        ),
        signature=QASignature()
    )

    # Enable full observability
    obs = agent.enable_observability(service_name="e2e-test-agent")

    # Run real LLM call
    result = agent.run(question="What is AI?")

    # Verify observability data
    assert result['answer']  # LLM responded

    # Verify metrics collected
    metrics = await obs.export_metrics()
    assert "agent_loop_duration_ms" in metrics

    # Verify audit trail
    entries = await obs.query_audit_by_agent("e2e-test-agent")
    assert len(entries) > 0

    agent.cleanup()
```

**Test 2: Multi-Agent Coordination with Observability**:
```python
@pytest.mark.tier3
async def test_multi_agent_coordination_observability():
    """Test observability across multiple coordinating agents."""
    # Create supervisor and workers with observability
    # Verify trace propagation across agents
    # Verify metrics aggregation
    # Verify audit trail completeness
```

**Test 3: Long-Running Agent with Observability**:
```python
@pytest.mark.tier3
@pytest.mark.slow
async def test_long_running_observability():
    """Test observability over extended period (1 hour)."""
    # Run agent continuously for 1 hour
    # Verify no memory leaks
    # Verify metrics accuracy over time
    # Verify audit file growth is manageable
```

**Success Criteria**:
- [ ] 5-10 E2E tests created
- [ ] Tests with real OpenAI calls
- [ ] Tests with real Anthropic calls
- [ ] Multi-agent coordination tested
- [ ] Long-running tests (1+ hour)
- [ ] All tests passing
- [ ] Budget allocated for API costs

**Files to Create**:
- `tests/e2e/observability/test_real_llm_integration.py`
- `tests/e2e/observability/test_multi_agent_observability.py`
- `tests/e2e/observability/test_long_running_observability.py`

---

## P1 - HIGH (Should Complete Before Production)

### 4. Infrastructure High Availability 🟠

**Status**: ⏳ Not Started
**Owner**: TBD
**Effort**: 1 week

**Current State**:
- ✅ Single-node Docker Compose works
- ❌ No redundancy
- ❌ No failover

**Required**:
- [ ] Deploy Prometheus cluster or managed service (e.g., Grafana Cloud, AWS Managed Prometheus)
- [ ] Deploy Jaeger with Elasticsearch backend (3+ node cluster)
- [ ] Deploy Grafana with HA (2+ replicas behind load balancer)
- [ ] Configure health checks and auto-restart
- [ ] Set up monitoring of observability stack itself

**Success Criteria**:
- [ ] All observability services have 2+ replicas
- [ ] Automatic failover tested
- [ ] Health monitoring in place
- [ ] Documented recovery procedures

**Files to Create**:
- `grafana/kubernetes/` - K8s manifests for HA deployment
- `docs/observability/HIGH_AVAILABILITY.md`

---

### 5. Audit Log Rotation & Retention 🟠

**Status**: ⏳ Not Started
**Owner**: TBD
**Effort**: 1 day

**Current State**:
- ✅ Audit logs append to `~/.kaizen/audit_trail.jsonl`
- ❌ No rotation (file grows indefinitely)
- ❌ No compression
- ❌ No archival
- ❌ No retention policy enforcement

**Required**:

**A. Daily Rotation**:
```python
# Use daily audit files
audit_file = f"~/.kaizen/audit_{datetime.now():%Y%m%d}.jsonl"
storage = FileAuditStorage(audit_file)
```

**B. Compression & Archival**:
```bash
#!/bin/bash
# cron job: /etc/cron.daily/kaizen-audit-rotate

AUDIT_DIR="$HOME/.kaizen"
ARCHIVE_DIR="$HOME/.kaizen/archive"

# Compress yesterday's audit log
yesterday=$(date -d "yesterday" +%Y%m%d)
gzip "$AUDIT_DIR/audit_$yesterday.jsonl"

# Move to archive
mv "$AUDIT_DIR/audit_$yesterday.jsonl.gz" "$ARCHIVE_DIR/"

# Upload to S3 (optional)
aws s3 cp "$ARCHIVE_DIR/audit_$yesterday.jsonl.gz" \
  s3://my-bucket/kaizen-audit-logs/

# Delete logs older than 90 days (adjust per retention policy)
find "$ARCHIVE_DIR" -name "audit_*.jsonl.gz" -mtime +90 -delete
```

**C. Retention Policy Enforcement**:
```python
# Add to AuditTrailManager
async def enforce_retention_policy(
    self,
    retention_days: int = 90
) -> int:
    """Delete audit entries older than retention period."""
    cutoff = datetime.now() - timedelta(days=retention_days)
    deleted_count = await self.storage.delete_before(cutoff)
    return deleted_count
```

**Success Criteria**:
- [ ] Daily rotation implemented
- [ ] Compression working
- [ ] Archival to cold storage (S3, etc.)
- [ ] Retention policy enforced (90+ days per compliance)
- [ ] Cron job or systemd timer configured
- [ ] Documented in README

**Files to Create**:
- `scripts/audit-rotate.sh`
- `docs/observability/AUDIT_RETENTION.md`

**Files to Update**:
- `src/kaizen/core/autonomy/observability/audit.py` - Add retention enforcement

---

### 6. ELK Stack Integration 🟠

**Status**: ⏳ Not Started
**Owner**: TBD
**Effort**: 2-3 days

**Current State**:
- ✅ JSON logs output to stdout
- ❌ No log aggregation
- ❌ No log search
- ❌ No log correlation with traces

**Required**:

**A. Add Logstash to Docker Compose**:
```yaml
# grafana/docker-compose.yml
logstash:
  image: docker.elastic.co/logstash/logstash:8.11.0
  volumes:
    - ./logstash.conf:/usr/share/logstash/pipeline/logstash.conf
  depends_on:
    - elasticsearch

elasticsearch:
  image: docker.elastic.co/elasticsearch/elasticsearch:8.11.0
  environment:
    - discovery.type=single-node
  ports:
    - "9200:9200"

kibana:
  image: docker.elastic.co/kibana/kibana:8.11.0
  ports:
    - "5601:5601"
  depends_on:
    - elasticsearch
```

**B. Configure Logstash Pipeline**:
```ruby
# grafana/logstash.conf
input {
  tcp {
    port => 5000
    codec => json_lines
  }
}

filter {
  # Parse Kaizen JSON logs
  json {
    source => "message"
  }

  # Extract trace_id for correlation
  mutate {
    add_field => { "[@metadata][trace_id]" => "%{trace_id}" }
  }
}

output {
  elasticsearch {
    hosts => ["elasticsearch:9200"]
    index => "kaizen-logs-%{+YYYY.MM.dd}"
  }
}
```

**C. Configure Agent Log Shipping**:
```python
# Configure structured logger to send to Logstash
import logging
from logging.handlers import SocketHandler

logger = logging.getLogger("kaizen")
logger.addHandler(SocketHandler("localhost", 5000))
```

**Success Criteria**:
- [ ] ELK Stack deployed (Elasticsearch, Logstash, Kibana)
- [ ] Logs shipped from agents to Logstash
- [ ] Logs searchable in Kibana
- [ ] Log correlation with traces working (trace_id)
- [ ] Kibana dashboards created
- [ ] Documented setup

**Files to Create**:
- `grafana/logstash.conf`
- `docs/observability/ELK_INTEGRATION.md`

**Files to Update**:
- `grafana/docker-compose.yml`

---

## P2 - MEDIUM (First Month of Production)

### 7. Distributed Audit Storage 🟡

**Status**: ⏳ Not Started
**Owner**: TBD
**Effort**: 1-2 weeks

**Current State**:
- ✅ FileAuditStorage works for single-node
- ❌ No database storage
- ❌ No S3 storage
- ❌ No distributed support

**Required**:

**A. DatabaseAuditStorage Implementation**:
```python
# src/kaizen/core/autonomy/observability/audit.py

class DatabaseAuditStorage(AuditStorage):
    """PostgreSQL-backed audit storage for distributed deployments."""

    def __init__(self, connection_string: str):
        self.conn_string = connection_string
        self._ensure_schema()

    async def append(self, entry: AuditEntry) -> None:
        """Append audit entry to database."""
        async with asyncpg.connect(self.conn_string) as conn:
            await conn.execute("""
                INSERT INTO audit_trail
                (timestamp, agent_id, action, details, result, user_id, metadata)
                VALUES ($1, $2, $3, $4, $5, $6, $7)
            """, entry.timestamp, entry.agent_id, entry.action,
               json.dumps(entry.details), entry.result,
               entry.user_id, json.dumps(entry.metadata or {}))

    async def query(...) -> list[AuditEntry]:
        """Query audit entries from database."""
        # Implementation with SQL query
```

**B. S3AuditStorage Implementation**:
```python
class S3AuditStorage(AuditStorage):
    """S3-backed audit storage for compliance archival."""

    def __init__(self, bucket: str, prefix: str):
        self.s3 = boto3.client('s3')
        self.bucket = bucket
        self.prefix = prefix

    async def append(self, entry: AuditEntry) -> None:
        """Append audit entry to S3 (daily files)."""
        key = f"{self.prefix}/{datetime.now():%Y/%m/%d}/audit.jsonl"
        # Append to S3 object (use S3 Select for queries)
```

**Success Criteria**:
- [ ] DatabaseAuditStorage implemented and tested
- [ ] S3AuditStorage implemented and tested
- [ ] Backward compatible with FileAuditStorage
- [ ] Performance validated (still <10ms p95)
- [ ] Documentation updated

**Files to Create**:
- `src/kaizen/core/autonomy/observability/storage/database.py`
- `src/kaizen/core/autonomy/observability/storage/s3.py`
- `tests/integration/observability/test_database_storage.py`
- `tests/integration/observability/test_s3_storage.py`

---

### 8. Operational Runbooks 🟡

**Status**: ⏳ Not Started
**Owner**: TBD
**Effort**: 1 week

**Current State**:
- ✅ Comprehensive documentation exists
- ❌ No incident response procedures
- ❌ No troubleshooting workflows

**Required Runbooks**:

**Runbook 1: High Observability Overhead**:
```markdown
# Runbook: High Observability Overhead Detected

## Symptoms
- Agent latency increased by >10%
- Grafana shows overhead >target thresholds
- User complaints about slow responses

## Diagnosis
1. Check Grafana "Performance Metrics" dashboard
2. Identify which component has high overhead
3. Check for: high metric cardinality, excessive logging, trace sampling rate

## Resolution
1. If metrics overhead >2%:
   - Reduce metric cardinality (fewer labels)
   - Increase aggregation period
   - Disable non-critical metrics
2. If logging overhead >5%:
   - Reduce log level (INFO → WARNING)
   - Disable verbose logging
   - Sample logs (log 1 in 10)
3. If tracing overhead >1%:
   - Enable sampling (trace 10% of requests)
   - Reduce span attributes

## Prevention
- Monitor overhead continuously
- Set alerts on overhead thresholds
- Regular performance reviews
```

**Runbook 2: Audit File Full**:
```markdown
# Runbook: Audit File Consuming Too Much Disk Space

## Symptoms
- Disk usage alerts
- `~/.kaizen/audit_trail.jsonl` >10GB
- Slow audit queries

## Resolution
1. Rotate audit log immediately
2. Compress old logs
3. Archive to S3/cold storage
4. Set up automatic rotation (cron)

## Prevention
- Enable daily rotation
- Set retention policy (90 days)
- Monitor disk usage
```

**Other Runbooks**:
- Jaeger Not Receiving Traces
- Prometheus Not Scraping Metrics
- Grafana Dashboard Showing No Data
- Memory Leak in Agent with Observability
- Compliance Audit Failed
- Audit Trail Integrity Verification Failed

**Success Criteria**:
- [ ] 6-10 runbooks created
- [ ] Each runbook tested in staging
- [ ] Runbooks accessible to on-call team
- [ ] Runbooks reviewed by operations team

**Files to Create**:
- `docs/observability/runbooks/high-overhead.md`
- `docs/observability/runbooks/audit-file-full.md`
- `docs/observability/runbooks/jaeger-no-traces.md`
- `docs/observability/runbooks/prometheus-no-scrape.md`
- `docs/observability/runbooks/grafana-no-data.md`
- `docs/observability/runbooks/memory-leak.md`

---

### 9. Load Testing 🟡

**Status**: ⏳ Not Started
**Owner**: TBD
**Effort**: 1 week

**Current State**:
- ✅ Unit and integration tests pass
- ❌ No load testing
- ❌ Unknown behavior under high volume

**Required Tests**:

**Test 1: Sustained Load (1000 req/min)**:
```python
# Load test with Locust
from locust import HttpUser, task, between

class KaizenAgentUser(HttpUser):
    wait_time = between(0.05, 0.1)  # 10-20 req/sec per user

    @task
    def ask_question(self):
        self.client.post("/agent/run", json={
            "question": "What is AI?"
        })

# Run: locust -f load_test.py --users 50 --spawn-rate 10
# Expected: 1000 req/min sustained for 1 hour
```

**Test 2: Spike Load (5000 req/min burst)**:
- Test sudden traffic spike
- Verify observability still works
- Check for dropped metrics/logs/traces

**Test 3: Multi-Agent Load**:
- 10 agents running simultaneously
- Each handling 100 req/min
- Verify observability scales

**Metrics to Collect**:
- Agent latency (p50, p95, p99)
- Observability overhead
- Memory usage over time
- CPU usage over time
- Disk usage growth
- Network bandwidth
- Error rate
- Throughput (req/sec)

**Success Criteria**:
- [ ] Sustained load test (1000 req/min, 1 hour)
- [ ] Spike load test (5000 req/min, 5 min)
- [ ] Multi-agent load test (10 agents, 30 min)
- [ ] All performance targets met
- [ ] No memory leaks detected
- [ ] No crashes or errors
- [ ] Results documented

**Files to Create**:
- `tests/load/locustfile.py`
- `docs/observability/LOAD_TEST_RESULTS.md`

---

## P3 - LOW (Nice to Have)

### 10. Video Tutorials 🟢

**Effort**: 1-2 weeks
**Owner**: TBD

**Videos to Create**:
1. "Quick Start: Enable Observability in 5 Minutes"
2. "Deploying the Observability Stack with Docker"
3. "Reading Grafana Dashboards"
4. "Investigating Performance Issues with Jaeger"
5. "Audit Trail Compliance Demo (SOC2, GDPR, HIPAA)"

---

### 11. Advanced Features 🟢

**Effort**: Ongoing
**Owner**: TBD

**Features**:
- Anomaly detection on metrics
- Auto-scaling based on metrics
- Cost attribution by user/agent
- SLA tracking and reporting
- Predictive alerting
- Automated remediation

---

### 12. Migration Guides 🟢

**Effort**: 2-3 days
**Owner**: TBD

**Guides to Create**:
- "Migrating from Kaizen v0.3.x to v0.4.x"
- "Migrating from Custom Logging to Observability System"
- "Migrating from File Audit Logs to Database Storage"

---

## Summary

### Critical Path to Production (P0)

1. ✅ Production Overhead Validation (COMPLETE - 1 day)
2. ⏳ Security Hardening (3-5 days)
3. ⏳ Tier 3 E2E Tests (3-5 days)

**Total Time Remaining**: 2-3 weeks

**Estimated Effort Remaining**: ~10-15 person-days

### Pre-Production Checklist

Before deploying to production, ensure:

- [x] **P0-1**: Production overhead validated with real workloads ✅ COMPLETE
- [ ] **P0-2**: Security hardening complete (TLS, auth, secrets)
- [ ] **P0-3**: Tier 3 E2E tests passing
- [ ] **P1-4**: High availability configured (or acceptable risk documented)
- [ ] **P1-5**: Audit log rotation implemented
- [ ] **P1-6**: ELK Stack integrated for log aggregation
- [ ] **P2-8**: At least 3 critical runbooks created
- [ ] **P2-9**: Load testing completed

### Current Readiness: 80%

- **Core Systems**: 100% ✅
- **Testing**: 85% (missing E2E) ⚠️
- **Infrastructure**: 70% (needs hardening) ⚠️
- **Documentation**: 95% ✅
- **Performance Validation**: 100% ✅ COMPLETE

### Recommended Deployment Strategy

**Week 1-2**: Complete P0 items
**Week 3**: Deploy to staging, run load tests
**Week 4**: Complete P1 items
**Week 5**: Deploy to production (canary rollout)
**Week 6-8**: Complete P2 items
**Ongoing**: P3 items as time permits

---

**Last Updated**: 2025-10-24
**Next Review**: After P0 completion
**Owner**: Observability Team
**Priority**: HIGH
