32.8. Cross-Functional Contracts: The Human API

Tip

Conway’s Law applied to ML: “Organizations which design systems are constrained to produce designs which are copies of the communication structures of these organizations.” If your Data Scientists don’t talk to your SREs, your Model Registry will be a dumpster fire.

While technical contracts (APIs) are rigidly enforced by compilers, social contracts (SLAs) are loosely enforced by managers. This semantic gap is where most MLOps initiatives fail. We need to formalize the relationship between the Creators (Data Science) and the Custodians (Platform Engineering).

32.8.1. The Operating Models: Who Owns What?

There are two primary operating models. You must explicitly choose one.

Model A: “You Build It, You Run It” (Spotify Model)

graph TB
    subgraph "Data Science Squad"
        A[Build Model] --> B[Package Container]
        B --> C[Deploy to K8s]
        C --> D[Monitor & On-Call]
    end
    
    subgraph "Platform Team"
        E[Provide EKS Cluster]
        F[Provide Monitoring Stack]
        G[Provide CI/CD Templates]
    end
    
    E --> C
    F --> D
    G --> B

• Philosophy: The Data Science Squad owns the model from Jupyter to Production.
• Platform Role: Provides the “Golden Path” (Self-service infrastructure). SREs manage the Kubernetes cluster, not the pods running on it.
• Contract: “Platform guarantees the EKS Control Plane uptime. DS guarantees the Python inference service logic.”

Advantages:

• Faster iteration (no handoffs)
• Clear ownership
• Teams learn Ops skills

Disadvantages:

• Requires skilled DS teams
• Inconsistent standards across squads
• Can lead to reinventing wheels

Model B: “The Handover” (Traditional Enterprise)

graph LR
    subgraph "Data Science"
        A[Research] --> B[Prototype]
        B --> C[Model Artifact]
    end
    
    subgraph "ML Engineering"
        D[Production Code] --> E[Container]
        E --> F[Deploy]
    end
    
    subgraph "Platform/SRE"
        G[Infrastructure]
        H[Monitoring]
        I[On-Call]
    end
    
    C -->|PRR| D
    F --> G
    F --> H
    H --> I

• Philosophy: DS builds a prototype; ML Engineers rewrite it for Prod.
• Contract: The Production Readiness Review (PRR).
  • No model crosses the “Air Gap” from Dev to Prod without passing the PRR Checklist.

Advantages:

• Clear separation of concerns
• Specialists at each stage
• Consistent production quality

Disadvantages:

• Slow handoffs
• Translation errors
• DS frustration (“they changed my model!”)

Hybrid Model: The Best of Both

# ownership_matrix.py - Define clear boundaries

from dataclasses import dataclass
from enum import Enum
from typing import Dict, List


class Responsibility(Enum):
    OWNS = "Owns"           # Primary responsibility
    SUPPORTS = "Supports"   # Secondary/consulting
    INFORMED = "Informed"   # Keep in loop only


@dataclass
class OwnershipMatrix:
    """Define team responsibilities for each phase."""
    
    activities: Dict[str, Dict[str, Responsibility]] = None
    
    def __post_init__(self):
        self.activities = {
            # Development Phase
            "Research & Experimentation": {
                "Data Science": Responsibility.OWNS,
                "ML Engineering": Responsibility.INFORMED,
                "Platform": Responsibility.INFORMED
            },
            "Feature Engineering": {
                "Data Science": Responsibility.OWNS,
                "ML Engineering": Responsibility.SUPPORTS,
                "Platform": Responsibility.INFORMED
            },
            "Model Training": {
                "Data Science": Responsibility.OWNS,
                "ML Engineering": Responsibility.SUPPORTS,
                "Platform": Responsibility.INFORMED
            },
            
            # Productionization Phase
            "Code Optimization": {
                "Data Science": Responsibility.SUPPORTS,
                "ML Engineering": Responsibility.OWNS,
                "Platform": Responsibility.INFORMED
            },
            "Containerization": {
                "Data Science": Responsibility.INFORMED,
                "ML Engineering": Responsibility.OWNS,
                "Platform": Responsibility.SUPPORTS
            },
            "CI/CD Pipeline": {
                "Data Science": Responsibility.INFORMED,
                "ML Engineering": Responsibility.OWNS,
                "Platform": Responsibility.SUPPORTS
            },
            
            # Operations Phase
            "Infrastructure Management": {
                "Data Science": Responsibility.INFORMED,
                "ML Engineering": Responsibility.INFORMED,
                "Platform": Responsibility.OWNS
            },
            "Model Monitoring": {
                "Data Science": Responsibility.OWNS,
                "ML Engineering": Responsibility.SUPPORTS,
                "Platform": Responsibility.INFORMED
            },
            "Incident Response (Model)": {
                "Data Science": Responsibility.OWNS,
                "ML Engineering": Responsibility.SUPPORTS,
                "Platform": Responsibility.INFORMED
            },
            "Incident Response (Infra)": {
                "Data Science": Responsibility.INFORMED,
                "ML Engineering": Responsibility.INFORMED,
                "Platform": Responsibility.OWNS
            }
        }
    
    def get_owner(self, activity: str) -> str:
        """Get primary owner for an activity."""
        if activity not in self.activities:
            raise ValueError(f"Unknown activity: {activity}")
        
        for team, resp in self.activities[activity].items():
            if resp == Responsibility.OWNS:
                return team
        return "Undefined"
    
    def generate_raci_matrix(self) -> str:
        """Generate RACI matrix as markdown table."""
        
        header = "| Activity | Data Science | ML Engineering | Platform |"
        separator = "|:---------|:-------------|:---------------|:---------|"
        
        rows = [header, separator]
        
        for activity, responsibilities in self.activities.items():
            row = f"| {activity} |"
            for team in ["Data Science", "ML Engineering", "Platform"]:
                resp = responsibilities.get(team, Responsibility.INFORMED)
                symbol = {
                    Responsibility.OWNS: "**A** (Owns)",
                    Responsibility.SUPPORTS: "C (Consult)",
                    Responsibility.INFORMED: "I"
                }[resp]
                row += f" {symbol} |"
            rows.append(row)
        
        return "\n".join(rows)

32.8.2. The Production Readiness Review (PRR) Checklist

The PRR is the formal contract for the Handover. It should be a Markdown document in the repo, signed off by both leads.

PRR Template

# Production Readiness Review
## Model: {{ model_name }}
## Version: {{ version }}
## Date: {{ date }}

---

## 1. Observability ✅

### Logging
- [ ] Structured JSON logging implemented
- [ ] Log levels appropriately set (INFO for prod)
- [ ] Request/response payloads logged (with PII redaction)
- [ ] Correlation IDs propagated

### Metrics
- [ ] Latency histogram exposed (p50, p95, p99)
- [ ] Request count exposed
- [ ] Error rate exposed
- [ ] Business metrics exposed (predictions by category)

### Dashboards
- [ ] Grafana dashboard created: [Link]
- [ ] PagerDuty alerts configured: [Link]
- [ ] Runbook created: [Link]

---

## 2. Reproducibility ✅

### Code
- [ ] Training code in version control: [Commit SHA]
- [ ] Inference code in version control: [Commit SHA]
- [ ] Docker image tagged: [Image SHA]

### Data
- [ ] Training data versioned (DVC/lakeFS): [Version]
- [ ] Feature definitions in Feature Store: [Link]
- [ ] Test dataset preserved for validation

### Model
- [ ] Model artifact in registry: [URI]
- [ ] Model card completed: [Link]
- [ ] Hyperparameters documented

---

## 3. Scalability & Performance ✅

### Load Testing
- [ ] Target throughput defined: {{ target_qps }} QPS
- [ ] Load test executed: [Results link]
- [ ] P99 latency under load: {{ p99_latency }}ms (SLA: {{ sla_latency }}ms)

### Resource Configuration
- [ ] Memory request: {{ memory_request }}
- [ ] Memory limit: {{ memory_limit }}
- [ ] CPU request: {{ cpu_request }}
- [ ] GPU requirement: {{ gpu_type }}

### Autoscaling
- [ ] HPA configured: min={{ min_replicas }}, max={{ max_replicas }}
- [ ] Scale-up threshold: {{ cpu_threshold }}% CPU
- [ ] Scale-down stabilization: {{ cooldown }}s

---

## 4. Failure Modes ✅

### Dependency Failures
| Dependency | Failure Behavior | Tested? |
|:-----------|:-----------------|:--------|
| Feature Store | Return cached value | ✅ |
| Model Server | Return default prediction | ✅ |
| Database | Fail open with fallback | ✅ |

### Graceful Degradation
- [ ] Circuit breaker implemented
- [ ] Timeout configured: {{ timeout_ms }}ms
- [ ] Retry policy: {{ retry_count }} attempts

### Rollback
- [ ] Previous version deployable in <5 min
- [ ] Rollback tested: [Date]

---

## 5. Cost Estimate ✅

| Resource | Unit Cost | Monthly Usage | Monthly Cost |
|:---------|:----------|:--------------|:-------------|
| Compute | ${{ cpu_cost }}/hr | {{ cpu_hours }} hrs | ${{ compute_total }} |
| GPU | ${{ gpu_cost }}/hr | {{ gpu_hours }} hrs | ${{ gpu_total }} |
| Storage | ${{ storage_cost }}/GB | {{ storage_gb }} GB | ${{ storage_total }} |
| **Total** | | | **${{ total_cost }}** |

- [ ] Cost below budget: ${{ budget }}
- [ ] CostCenter tag applied: {{ cost_center }}

---

## 6. Security ✅

- [ ] No secrets in code
- [ ] IAM role follows least privilege
- [ ] Input validation implemented
- [ ] Rate limiting configured

---

## Approvals

| Role | Name | Signature | Date |
|:-----|:-----|:----------|:-----|
| Data Science Lead | | | |
| ML Engineering Lead | | | |
| Platform Lead | | | |
| Product Manager | | | |

Automated PRR Enforcement

# prr_validator.py - Automate PRR checks in CI/CD

import yaml
from dataclasses import dataclass, field
from typing import List, Dict, Optional
from pathlib import Path
import subprocess
import json


@dataclass
class PRRCheck:
    name: str
    passed: bool
    details: str
    blocking: bool = True


@dataclass 
class PRRResult:
    model_name: str
    version: str
    checks: List[PRRCheck] = field(default_factory=list)
    
    @property
    def passed(self) -> bool:
        return all(c.passed for c in self.checks if c.blocking)
    
    @property
    def blocking_failures(self) -> List[PRRCheck]:
        return [c for c in self.checks if not c.passed and c.blocking]


class PRRValidator:
    """Automated Production Readiness Review validator."""
    
    def __init__(self, config_path: str):
        with open(config_path) as f:
            self.config = yaml.safe_load(f)
    
    def validate(
        self,
        model_path: str,
        deployment_config: Dict
    ) -> PRRResult:
        """Run all PRR checks."""
        
        result = PRRResult(
            model_name=deployment_config.get("model_name", "unknown"),
            version=deployment_config.get("version", "unknown")
        )
        
        # Check 1: Observability
        result.checks.append(self._check_logging(model_path))
        result.checks.append(self._check_metrics(model_path))
        result.checks.append(self._check_dashboard(deployment_config))
        
        # Check 2: Reproducibility
        result.checks.append(self._check_versioning(model_path))
        result.checks.append(self._check_model_card(model_path))
        
        # Check 3: Performance
        result.checks.append(self._check_load_test(deployment_config))
        result.checks.append(self._check_resource_limits(deployment_config))
        
        # Check 4: Failure Modes
        result.checks.append(self._check_circuit_breaker(model_path))
        result.checks.append(self._check_rollback_plan(deployment_config))
        
        # Check 5: Cost
        result.checks.append(self._check_cost_estimate(deployment_config))
        
        # Check 6: Security
        result.checks.append(self._check_secrets(model_path))
        result.checks.append(self._check_iam_policy(deployment_config))
        
        return result
    
    def _check_logging(self, model_path: str) -> PRRCheck:
        """Verify structured logging is implemented."""
        
        # Search for logging patterns in code
        import_patterns = [
            "import logging",
            "import structlog",
            "from loguru import logger"
        ]
        
        code_files = list(Path(model_path).rglob("*.py"))
        has_logging = False
        
        for file in code_files:
            content = file.read_text()
            if any(p in content for p in import_patterns):
                has_logging = True
                break
        
        return PRRCheck(
            name="Structured Logging",
            passed=has_logging,
            details="Found logging implementation" if has_logging else "No logging found",
            blocking=True
        )
    
    def _check_metrics(self, model_path: str) -> PRRCheck:
        """Verify metrics are exposed."""
        
        metric_patterns = [
            "prometheus_client",
            "opentelemetry",
            "from datadog import"
        ]
        
        code_files = list(Path(model_path).rglob("*.py"))
        has_metrics = False
        
        for file in code_files:
            content = file.read_text()
            if any(p in content for p in metric_patterns):
                has_metrics = True
                break
        
        return PRRCheck(
            name="Metrics Exposed",
            passed=has_metrics,
            details="Found metrics implementation" if has_metrics else "No metrics found",
            blocking=True
        )
    
    def _check_load_test(self, config: Dict) -> PRRCheck:
        """Verify load test was performed."""
        
        load_test_results = config.get("load_test_results")
        
        if not load_test_results:
            return PRRCheck(
                name="Load Test",
                passed=False,
                details="No load test results provided",
                blocking=True
            )
        
        p99_latency = load_test_results.get("p99_latency_ms", float("inf"))
        sla_latency = config.get("sla_latency_ms", 500)
        
        passed = p99_latency <= sla_latency
        
        return PRRCheck(
            name="Load Test",
            passed=passed,
            details=f"P99: {p99_latency}ms (SLA: {sla_latency}ms)",
            blocking=True
        )
    
    def _check_cost_estimate(self, config: Dict) -> PRRCheck:
        """Verify cost is within budget."""
        
        estimated_cost = config.get("estimated_monthly_cost", 0)
        budget = config.get("monthly_budget", float("inf"))
        
        passed = estimated_cost <= budget
        
        return PRRCheck(
            name="Cost Estimate",
            passed=passed,
            details=f"Estimated: ${estimated_cost}/mo (Budget: ${budget}/mo)",
            blocking=True
        )
    
    def _check_secrets(self, model_path: str) -> PRRCheck:
        """Verify no secrets in code."""
        
        # Run secret detection
        try:
            result = subprocess.run(
                ["detect-secrets", "scan", model_path],
                capture_output=True,
                text=True
            )
            findings = json.loads(result.stdout)
            secrets_found = len(findings.get("results", {})) > 0
        except:
            secrets_found = False  # Tool not available, manual check needed
        
        return PRRCheck(
            name="No Secrets in Code",
            passed=not secrets_found,
            details="No secrets detected" if not secrets_found else "SECRETS FOUND!",
            blocking=True
        )
    
    # Additional check methods would follow the same pattern...
    
    def generate_report(self, result: PRRResult) -> str:
        """Generate PRR report in markdown."""
        
        status = "✅ PASSED" if result.passed else "❌ FAILED"
        
        report = f"""
# Production Readiness Review Report
## Model: {result.model_name} v{result.version}
## Status: {status}

---

## Check Results

| Check | Status | Details |
|:------|:-------|:--------|
"""
        
        for check in result.checks:
            emoji = "✅" if check.passed else ("⚠️" if not check.blocking else "❌")
            report += f"| {check.name} | {emoji} | {check.details} |\n"
        
        if result.blocking_failures:
            report += "\n## Blocking Issues\n\n"
            for failure in result.blocking_failures:
                report += f"- **{failure.name}**: {failure.details}\n"
        
        return report

32.8.3. Incident Response Contracts (SLAs)

When the model breaks at 3 AM, whose pager goes off?

The RACI Matrix for MLOps

On-Call Policies

# oncall_policy.yaml

policies:
  platform_team:
    coverage: 24x7
    response_time: 15_minutes
    responsibilities:
      - kubernetes_control_plane
      - networking
      - iam_and_security
      - monitoring_infrastructure
      - feature_store_availability
    escalation:
      - level_1: on_call_engineer
      - level_2: platform_lead
      - level_3: engineering_director
      
  ml_team:
    coverage: business_hours  # 9-6 local time
    after_hours: best_effort  # Unless revenue-critical
    response_time: 1_hour
    responsibilities:
      - model_accuracy
      - inference_logic
      - data_drift
      - prediction_quality
    escalation:
      - level_1: model_owner
      - level_2: ml_lead
      - level_3: data_science_director
      
  revenue_critical_models:
    # Override for specific models
    models:
      - fraud_detection
      - real_time_bidding
      - dynamic_pricing
    coverage: 24x7
    response_time: 15_minutes
    on_call_team: ml_team_critical

Runbook Template

# Incident Runbook: [CRITICAL] P99 Latency High on Fraud Model

## Trigger
- `fraud_model_latency_p99 > 500ms` for 5 minutes
- Alert source: PagerDuty
- Severity: P1

---

## Quick Diagnosis (< 5 minutes)

### Step 1: Check Traffic Volume
**Dashboard**: [Grafana - Fraud Model](link)

Is RPS > 2x normal?
- **YES**: Traffic spike. Check if HPA is scaling. Go to Step 4.
- **NO**: Proceed to Step 2.

### Step 2: Check Dependencies
**Dashboard**: [Dependency Health](link)

| Dependency | Status Check |
|:-----------|:-------------|
| Feature Store | [Tecton Status](link) |
| Database | [RDS CloudWatch](link) |
| Model Artifact S3 | [S3 Status](link) |

- **Any Degraded?**: Escalate to Platform Team. Stop here.
- **All Healthy**: Proceed to Step 3.

### Step 3: Check Model Resources
**Dashboard**: [Pod Resources](link)

| Metric | Healthy | Current |
|:-------|:--------|:--------|
| CPU | <80% | __% |
| Memory | <90% | __% |
| GPU | <95% | __% |

- **Resources Saturated?**: Go to Step 5 (Scale).
- **Resources OK**: Go to Step 6 (Bad Release).

### Step 4: Check Autoscaler
```bash
kubectl get hpa fraud-model -n ml-serving
kubectl describe hpa fraud-model -n ml-serving

• Max Replicas Hit?: Increase max replicas.

kubectl patch hpa fraud-model -n ml-serving --patch '{"spec":{"maxReplicas":100}}'

Step 5: Manual Scale

kubectl scale deployment fraud-model -n ml-serving --replicas=50

Monitor for 2 minutes. If latency drops, incident mitigated.

Step 6: Check Recent Deployments

kubectl rollout history deployment/fraud-model -n ml-serving

Was there a deployment in the last hour?

• YES: Rollback immediately.

kubectl rollout undo deployment/fraud-model -n ml-serving

Mitigation Options

Option A: Enable Degraded Mode

Serve cached predictions from last known good state.

kubectl set env deployment/fraud-model DEGRADED_MODE=true -n ml-serving

Option B: Shed Load

Enable rate limiting if traffic is the issue.

kubectl annotate ingress fraud-model nginx.ingress.kubernetes.io/limit-rps="100" -n ml-serving

Escalation

Post-Incident

• Timeline documented in incident ticket
• Root cause identified
• Action items created
• Post-mortem scheduled (for P1/P2)

---

## 32.8.4. Cost Attribution Contracts (FinOps)

"Who pays for the GPU?" In the cloud, it is easy to burn $100k in a weekend.

### Tagging Strategy

```hcl
# terraform/modules/ml-project/main.tf

locals {
  required_tags = {
    Environment   = var.environment
    Project       = var.project_name
    CostCenter    = var.cost_center
    Team          = var.team_name
    ModelName     = var.model_name
    ManagedBy     = "terraform"
    CreatedBy     = var.created_by
  }
}

# Enforce tagging on all resources
resource "aws_sagemaker_endpoint" "model" {
  name                 = var.endpoint_name
  endpoint_config_name = aws_sagemaker_endpoint_configuration.config.name
  
  tags = merge(local.required_tags, {
    ResourceType = "inference-endpoint"
    SLA         = var.sla_tier
  })
}

# S3 bucket policy to deny untagged writes
data "aws_iam_policy_document" "require_tags" {
  statement {
    sid    = "DenyUntaggedObjects"
    effect = "Deny"
    
    principals {
      type        = "*"
      identifiers = ["*"]
    }
    
    actions = ["s3:PutObject"]
    
    resources = ["${aws_s3_bucket.models.arn}/*"]
    
    condition {
      test     = "Null"
      variable = "s3:RequestObjectTag/CostCenter"
      values   = ["true"]
    }
  }
}

Budget Automation

# finops/budget_enforcer.py

import boto3
from datetime import datetime
from typing import Dict, List
import json


class BudgetEnforcer:
    """Automatically enforce ML cost budgets."""
    
    def __init__(self, account_id: str):
        self.account_id = account_id
        self.budgets = boto3.client('budgets')
        self.sagemaker = boto3.client('sagemaker')
        self.sns = boto3.client('sns')
    
    def create_project_budget(
        self,
        project_id: str,
        monthly_limit: float,
        alert_emails: List[str],
        auto_stop_threshold: float = 0.95  # 95% of budget
    ):
        """Create budget with alerts and auto-stop."""
        
        self.budgets.create_budget(
            AccountId=self.account_id,
            Budget={
                'BudgetName': f'ML-{project_id}',
                'BudgetLimit': {
                    'Amount': str(monthly_limit),
                    'Unit': 'USD'
                },
                'CostFilters': {
                    'TagKeyValue': [f'user:Project${project_id}']
                },
                'TimeUnit': 'MONTHLY',
                'BudgetType': 'COST'
            },
            NotificationsWithSubscribers=[
                # 50% alert
                {
                    'Notification': {
                        'NotificationType': 'ACTUAL',
                        'ComparisonOperator': 'GREATER_THAN',
                        'Threshold': 50,
                        'ThresholdType': 'PERCENTAGE'
                    },
                    'Subscribers': [
                        {'SubscriptionType': 'EMAIL', 'Address': email}
                        for email in alert_emails
                    ]
                },
                # 80% alert
                {
                    'Notification': {
                        'NotificationType': 'ACTUAL',
                        'ComparisonOperator': 'GREATER_THAN',
                        'Threshold': 80,
                        'ThresholdType': 'PERCENTAGE'
                    },
                    'Subscribers': [
                        {'SubscriptionType': 'EMAIL', 'Address': email}
                        for email in alert_emails
                    ]
                },
                # Auto-stop at 95%
                {
                    'Notification': {
                        'NotificationType': 'ACTUAL',
                        'ComparisonOperator': 'GREATER_THAN',
                        'Threshold': auto_stop_threshold * 100,
                        'ThresholdType': 'PERCENTAGE'
                    },
                    'Subscribers': [
                        {
                            'SubscriptionType': 'SNS',
                            'Address': self._get_auto_stop_topic()
                        }
                    ]
                }
            ]
        )
    
    def _get_auto_stop_topic(self) -> str:
        """Get or create SNS topic for auto-stop."""
        # This topic triggers Lambda to stop resources
        return f"arn:aws:sns:{self.region}:{self.account_id}:ml-budget-auto-stop"
    
    def stop_project_resources(self, project_id: str):
        """Stop all running resources for a project."""
        
        stopped_resources = []
        
        # Stop training jobs
        training_jobs = self.sagemaker.list_training_jobs(
            StatusEquals='InProgress'
        )['TrainingJobSummaries']
        
        for job in training_jobs:
            job_details = self.sagemaker.describe_training_job(
                TrainingJobName=job['TrainingJobName']
            )
            
            if self._matches_project(job_details, project_id):
                self.sagemaker.stop_training_job(
                    TrainingJobName=job['TrainingJobName']
                )
                stopped_resources.append(('TrainingJob', job['TrainingJobName']))
        
        # Stop endpoints (expensive!)
        endpoints = self.sagemaker.list_endpoints()['Endpoints']
        
        for endpoint in endpoints:
            endpoint_tags = self.sagemaker.list_tags(
                ResourceArn=endpoint['EndpointArn']
            )['Tags']
            
            if any(t['Key'] == 'Project' and t['Value'] == project_id for t in endpoint_tags):
                # Don't delete, but scale to 0
                self._scale_endpoint_to_zero(endpoint['EndpointName'])
                stopped_resources.append(('Endpoint', endpoint['EndpointName']))
        
        return stopped_resources

32.8.5. Versioning Policies and Deprecation

Data Science moves fast. APIs need stability. We need a policy for Deprecation.

The Model API Contract

# api_contract.yaml

model_api:
  name: fraud_detection
  current_version: v3
  supported_versions:
    - version: v3
      status: current
      end_of_life: null
    - version: v2
      status: deprecated
      end_of_life: "2024-06-01"
      migration_guide: "docs/v2-to-v3-migration.md"
    - version: v1
      status: sunset
      end_of_life: "2024-01-01"
      
  deprecation_policy:
    notice_period_days: 90
    support_previous_versions: 2
    brownout_testing: true
    
  sla:
    availability: 99.9%
    latency_p99: 200ms
    error_rate: 0.1%

Deprecation Workflow

graph LR
    A[T-90: Announce] --> B[T-60: Brownout Test]
    B --> C[T-30: Blackout Test]
    C --> D[T-0: Delete]
    
    B -.->|Consumer Issues| E[Extend Timeline]
    E --> B

32.8.6. Summary Checklist for Human Contracts

Social contracts prevent burnout and blame culture. Invest in them.

[End of Section 32.8]