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Tuesday, 18 November 2025
When your workflows learn to walk without training wheels (and you stop paying for babysitters)
Note: This is Part 2 in the "Cooking with DiSE" series, exploring practical patterns for production-scale workflow evolution. Today we're talking about something that sounds obvious in hindsight but is weirdly uncommon: workflows that start with supervision, prove themselves, then graduate to run independently—until they need help again.
Here's something ridiculous about how we run AI workflows in production today:
Your workflow: *executes perfectly for the 1,000th time*
Your monitoring AI: "Yep, still perfect! That'll be $0.15."
Your workflow: *executes perfectly for the 1,001st time*
Your monitoring AI: "Still good! Another $0.15 please."
Your workflow: *executes perfectly for the 1,002nd time*
Your monitoring AI: "Perfect again! $0.15."
Monthly cost: $450 to watch perfection happen
Value provided: Approximately zero
We've normalized paying for monitoring that provides no value.
Not "low value." Zero value.
When a workflow has executed successfully 1,000 times with the same quality metrics, the same performance characteristics, the same everything—why are we still paying an AI to watch it?
It's like hiring a lifeguard to watch Olympic swimmers practice in a kiddie pool.
But here's what makes it worse: when things DO go wrong, our current monitoring often misses it anyway.
Because static monitoring looks for known patterns. Predefined thresholds. Expected failure modes.
What we actually need is the opposite:
This is the Apprenticeship Pattern.
Think about how humans learn a new job:
Week 1 (Apprentice): Senior watches everything you do, corrects mistakes in real-time
Week 4 (Intermediate): Senior checks in periodically, reviews output
Week 12 (Graduate): You work independently, senior only involved if something weird happens
Week 52 (Expert): You barely need supervision unless the job itself changes
Workflows should follow the same pattern.
graph TB
Start[New Workflow v1.0.0] --> Monitor[Monitoring AI Layer<br/>ACTIVE]
Monitor --> Step1[Tool Call: fetch_data]
Monitor --> Step2[Tool Call: process]
Monitor --> Step3[Tool Call: validate]
Monitor --> Step4[Tool Call: send_results]
Step1 --> Eval1[Quality Check<br/>Response time: 234ms<br/>Data completeness: 100%<br/>Error rate: 0%]
Step2 --> Eval2[Quality Check<br/>Processing accuracy: 99.7%<br/>Memory usage: 45MB<br/>CPU: 23%]
Step3 --> Eval3[Quality Check<br/>Validation pass: 100%<br/>Schema compliance: ✓<br/>Business rules: ✓]
Step4 --> Eval4[Quality Check<br/>Delivery success: 100%<br/>Latency: 156ms<br/>Format: valid JSON]
Eval1 --> Learn[Learning System]
Eval2 --> Learn
Eval3 --> Learn
Eval4 --> Learn
Learn --> Profile[Build Quality Profile<br/>Execution: 1/50 required]
Profile --> Decision{Iterations < 50?}
Decision --> |Yes| Monitor
Decision --> |No| Graduate[Graduate to Phase 2]
style Monitor stroke:#f57c00,stroke-width:3px
style Learn stroke:#0277bd,stroke-width:2px
style Graduate stroke:#2e7d32,stroke-width:3px
In Apprentice Mode:
Every single tool call is instrumented:
class ApprenticeWorkflow:
def __init__(self, workflow_id: str):
self.workflow_id = workflow_id
self.monitoring_tier = MonitoringTier.FULL # Expensive!
self.quality_profile = QualityProfile()
self.execution_count = 0
self.required_successes = 50 # Configurable
async def execute_tool(self, tool_name: str, params: dict):
"""Execute with full monitoring and learning"""
# Pre-execution baseline
baseline = await self.capture_baseline()
# Execute the tool
start_time = time.time()
result = await call_tool(tool_name, params)
execution_time = time.time() - start_time
# Post-execution analysis (THIS IS EXPENSIVE)
quality_check = await self.monitoring_ai.analyze(
tool_name=tool_name,
params=params,
result=result,
execution_time=execution_time,
baseline=baseline,
quality_profile=self.quality_profile
)
# Learn from this execution
self.quality_profile.update(
tool_name=tool_name,
metrics={
"execution_time": execution_time,
"result_size": len(str(result)),
"quality_score": quality_check.score,
"resource_usage": quality_check.resources,
"output_characteristics": quality_check.characteristics
}
)
return result, quality_check
The Monitoring AI (fast model with escalation):
class MonitoringAI:
def __init__(self):
self.fast_model = "gemma2:2b" # Quick checks
self.medium_model = "llama3:8b" # Deeper analysis
self.expensive_model = "claude-3.5-sonnet" # Full investigation
async def analyze(self, **context):
"""Tiered monitoring with escalation"""
# Tier 1: Fast checks (always run)
quick_check = await self.quick_analysis(context)
if quick_check.confidence > 0.95:
# We're confident it's fine or definitely broken
return quick_check
# Tier 2: Deeper analysis (escalate if uncertain)
medium_check = await self.medium_analysis(context)
if medium_check.confidence > 0.90:
return medium_check
# Tier 3: Full investigation (expensive, rare)
full_check = await self.expensive_analysis(context)
return full_check
async def quick_analysis(self, context):
"""Fast pass/fail classification"""
prompt = f"""
Quick quality check for tool execution:
Tool: {context['tool_name']}
Execution time: {context['execution_time']}ms
Expected range: {context['quality_profile'].get_expected_range()}
Is this execution within normal parameters?
Answer: NORMAL | SUSPICIOUS | BROKEN
Confidence: 0.0-1.0
"""
response = await call_llm(self.fast_model, prompt)
return AnalysisResult(
status=response.status,
confidence=response.confidence,
cost=0.001, # Very cheap
tier="fast"
)
The cost during apprenticeship:
Execution #1:
- Tool execution: 234ms, $0
- Fast monitoring: 45ms, $0.001
- Medium monitoring: (escalated) 180ms, $0.015
- Learning update: 12ms, $0
Total: 471ms, $0.016
Execution #2:
- Tool execution: 229ms, $0
- Fast monitoring: 43ms, $0.001
- Medium monitoring: (escalated) 175ms, $0.015
- Learning update: 11ms, $0
Total: 458ms, $0.016
[... repeated 48 more times ...]
Total Apprenticeship Cost:
50 executions × $0.016 = $0.80
Total time investment: ~23 seconds
Quality profile learned:
✓ Normal execution time: 225ms ± 15ms
✓ Normal output size: 1.2KB ± 200 bytes
✓ Normal resource usage: 45MB ± 5MB
✓ Success patterns: 50/50 perfect
✓ Failure patterns: 0/50 (none seen yet)
This is expensive! But it's also finite and valuable.
We're paying to learn what "good" looks like. That knowledge is permanent.
After 50 successful executions with consistent quality, the workflow graduates:
graph TB
Start[Graduated Workflow v1.0.0] --> Lite[Statistical Monitoring<br/>NO AI COST]
Lite --> Step1[Tool Call: fetch_data<br/>Time: 231ms ✓<br/>Size: 1.18KB ✓]
Lite --> Step2[Tool Call: process<br/>Time: 89ms ✓<br/>Memory: 44MB ✓]
Lite --> Step3[Tool Call: validate<br/>Pass: 100% ✓<br/>Rules: OK ✓]
Lite --> Step4[Tool Call: send_results<br/>Success: ✓<br/>Latency: 152ms ✓]
Step1 --> Check{Within<br/>profile?}
Step2 --> Check
Step3 --> Check
Step4 --> Check
Check --> |All ✓| Success[Execution Complete<br/>Cost: $0.00]
Check --> |Drift| Alert[Drift Detected!<br/>Re-engage monitoring]
Alert --> Diagnose[Monitoring AI<br/>Investigates]
Diagnose --> Fix[Auto-Fix or Escalate]
style Lite stroke:#388e3c,stroke-width:2px
style Success stroke:#2e7d32,stroke-width:3px
style Alert stroke:#f57c00,stroke-width:2px
The monitoring is now purely statistical:
class GraduatedWorkflow:
def __init__(self, workflow_id: str, quality_profile: QualityProfile):
self.workflow_id = workflow_id
self.monitoring_tier = MonitoringTier.STATISTICAL # FREE!
self.quality_profile = quality_profile
self.drift_detector = DriftDetector(quality_profile)
async def execute_tool(self, tool_name: str, params: dict):
"""Execute with lightweight statistical monitoring"""
# Execute the tool (same as before)
start_time = time.time()
result = await call_tool(tool_name, params)
execution_time = time.time() - start_time
# NO AI MONITORING - Just compare to profile
metrics = {
"execution_time": execution_time,
"result_size": len(str(result)),
"timestamp": datetime.now()
}
# Statistical drift detection (milliseconds, zero cost)
drift_score = self.drift_detector.check(tool_name, metrics)
if drift_score < 0.1: # Within normal bounds
return result, MonitoringResult(
status="OK",
cost=0.0, # FREE!
drift_score=drift_score
)
# DRIFT DETECTED - Re-engage monitoring AI
alert = await self.handle_drift(tool_name, metrics, drift_score)
return result, alert
async def handle_drift(self, tool_name, metrics, drift_score):
"""Drift detected - engage monitoring AI to diagnose"""
# This is the ONLY time we pay for AI monitoring
diagnosis = await self.monitoring_ai.investigate_drift(
tool_name=tool_name,
current_metrics=metrics,
expected_profile=self.quality_profile.get_profile(tool_name),
drift_score=drift_score,
recent_executions=self.get_recent_executions(tool_name, n=10)
)
# Return diagnosis with recommended action
return DriftAlert(
drift_score=drift_score,
diagnosis=diagnosis,
recommended_action=diagnosis.action,
cost=diagnosis.cost # Only paid when drift detected!
)
The cost after graduation:
Execution #51 (graduated):
- Tool execution: 228ms, $0
- Statistical monitoring: 0.3ms, $0
- AI monitoring: SKIPPED, $0
Total: 228ms, $0.00
Execution #52:
- Tool execution: 231ms, $0
- Statistical monitoring: 0.3ms, $0
- AI monitoring: SKIPPED, $0
Total: 231ms, $0.00
[... repeated 948 more times ...]
Execution #1000:
- Tool execution: 226ms, $0
- Statistical monitoring: 0.3ms, $0
- AI monitoring: SKIPPED, $0
Total: 226ms, $0.00
Total Cost (Executions 51-1000):
950 executions × $0.00 = $0.00
Drift detections: 0
AI monitoring engaged: 0 times
Total monitoring cost: $0.00
We went from $0.016 per execution to $0.00.
For a workflow running 10,000 times per day:
Annual savings: $58,000 per workflow.
But here's where it gets interesting. What happens when something changes?
Execution #1,247:
- Tool execution: 228ms, $0
- Statistical monitoring: 0.3ms, $0
- Drift score: 0.02 (normal)
- AI monitoring: SKIPPED
Execution #1,248:
- Tool execution: 892ms, $0 ← WHOA
- Statistical monitoring: 0.3ms, $0
- Drift score: 0.47 (DRIFT DETECTED!)
- AI monitoring: ENGAGED!
Monitoring AI investigation:
Analyzing drift...
✓ Execution time: 892ms (expected: 225ms ± 15ms)
✓ Drift magnitude: 296% increase
✓ Result correctness: Unchanged
✓ Output size: Normal
✓ Error rate: 0%
Diagnosis: External API latency increased
Evidence:
- fetch_data tool calling external API
- API response time: 750ms (was 100ms)
- API behavior changed but output still valid
Trend analysis:
- Last 5 executions: 892ms, 876ms, 901ms, 888ms, 894ms
- Consistent elevated latency
- Not intermittent - PERMANENT SHIFT
Recommended action: UPDATE_PROFILE
Reason: API has permanently slowed, workflow still correct
Cost: $0.025 (one-time)
The system detected a permanent shift and adapted its quality profile:
class DriftDetector:
async def handle_consistent_drift(
self,
tool_name: str,
diagnosis: Diagnosis
):
"""Handle drift that represents a new normal"""
if diagnosis.action == "UPDATE_PROFILE":
# The world changed, workflow is still correct
# Update our expectations
self.quality_profile.update_baseline(
tool_name=tool_name,
new_metrics=diagnosis.new_normal,
reason=diagnosis.reason
)
logger.info(
f"Quality profile updated for {tool_name}: "
f"{diagnosis.reason}"
)
return ProfileUpdateResult(
action="updated",
cost=diagnosis.cost, # One-time
future_cost=0.0 # Back to free monitoring
)
Cost:
Drift detection: 1 event
AI investigation: 1 × $0.025 = $0.025
Profile update: 1 × $0 = $0
Total: $0.025 (one-time)
Future executions: Back to $0.00 each
We paid $0.025 once to adapt to a changed environment.
Execution #2,847:
- Tool execution: 229ms, $0
- Validation pass: 100%
- Drift score: 0.03 (normal)
Execution #2,848:
- Tool execution: 231ms, $0
- Validation pass: 94% ← Hmm
- Drift score: 0.12 (minor drift)
- AI monitoring: ENGAGED (Tier 1)
Fast Monitoring AI:
Quick check: Validation pass rate dropped from 100% to 94%
Confidence: 0.72 (not confident - ESCALATE)
Cost: $0.001
Medium Monitoring AI:
Detailed analysis:
- Last 10 executions: 94%, 92%, 100%, 89%, 91%, 100%, 87%, 93%, 100%, 85%
- Trend: DEGRADING (5% drop over 10 runs)
- Root cause: Input data quality decreased
- Workflow correctness: Still OK, but fragile
- Recommendation: EVOLVE_WORKFLOW
Confidence: 0.94 (high confidence)
Cost: $0.015
Evolution Triggered:
Strategy: Strengthen validation rules
Approach: Add input sanitization step
Estimated improvement: +8% validation pass rate
Mutation generated:
- New step: sanitize_input (before process)
- Tool: input_sanitizer_v1.0.0 (generated)
- Expected impact: Reduce invalid inputs by 80%
Cost: $0.050 (one-time generation)
The system:
Total cost: $0.066 (one-time)
Execution #4,521:
- Tool execution: 234ms, $0
- Result: SUCCESS
- Drift score: 0.02 (normal)
Execution #4,522:
- Tool execution: EXCEPTION
- Error: "API returned 500 Internal Server Error"
- Drift score: 1.0 (MAXIMUM DRIFT!)
- AI monitoring: ENGAGED (All tiers)
Fast Monitoring AI:
Quick check: CRITICAL FAILURE
Confidence: 1.0 (certain)
Escalate: YES
Cost: $0.001
Medium Monitoring AI:
Analysis: External API is down
Confidence: 0.98
Escalate: YES (need recovery strategy)
Cost: $0.015
Expensive Monitoring AI (claude-3.5-sonnet):
Diagnosis:
- API: example-api.com/v1/process
- Status: HTTP 500 (Internal Server Error)
- Duration: Started 3 minutes ago
- Impact: ALL workflows using this API
- Historical pattern: API has had 3 outages in last 6 months
Recommended actions:
1. IMMEDIATE: Add retry logic with exponential backoff
2. SHORT-TERM: Implement circuit breaker pattern
3. LONG-TERM: Add fallback to alternative API
Implementation:
- Generate retry_wrapper tool with 3 attempts, exp backoff
- Wrap existing API call with retry logic
- Add circuit breaker after 5 consecutive failures
- Estimated downtime reduction: 95%
Mutation generated:
- New workflow v1.1.0 with resilience
- Tools added: retry_wrapper, circuit_breaker
- Fallback: graceful degradation if API unavailable
Cost: $0.125 (comprehensive analysis + mutation)
The system:
Total cost: $0.141 (one-time, saves hours of debugging)
Let's do the math for a production system:
Workflow: process_customer_order
Execution frequency: 50,000 times/day
Uptime requirement: 99.9%
Cost per execution:
- Workflow execution: $0 (internal tools)
- AI monitoring: $0.01 (watch every execution)
Daily cost: 50,000 × $0.01 = $500/day
Annual cost: $182,500/year
Value provided:
- Catches maybe 10 issues per year
- Cost per issue caught: $18,250
- Most issues: False positives or minor
Phase 1: Apprenticeship (Days 1-2)
- Executions: 100 (learning phase)
- Cost per execution: $0.016
- Total: $1.60
Phase 2: Graduated Operation (Days 3-365)
- Executions: 50,000 × 363 days = 18,150,000
- Cost per execution: $0.00
- Total: $0.00
Drift events (estimated: 12 per year)
- Minor drift (profile update): 8 × $0.025 = $0.20
- Quality degradation (evolution): 3 × $0.066 = $0.20
- Critical failure (major fix): 1 × $0.141 = $0.14
- Total: $0.54
Annual total: $1.60 + $0.00 + $0.54 = $2.14
Savings: $182,498 per year. PER WORKFLOW.
For a company with 100 workflows: $18.2M annual savings.
Here's where the Apprenticeship Pattern gets really interesting. It's not just about saving money on monitoring.
It's about proactive evolution based on trends.
class ProactiveEvolver:
def analyze_graduated_workflow(self, workflow_id: str):
"""Analyze trends in graduated workflows"""
recent_executions = self.get_executions(workflow_id, days=30)
# Statistical analysis of trends
trends = {
"latency": self.analyze_latency_trend(recent_executions),
"quality": self.analyze_quality_trend(recent_executions),
"resource": self.analyze_resource_trend(recent_executions),
"success_rate": self.analyze_success_trend(recent_executions)
}
# Detect gradual degradation BEFORE it becomes a problem
warnings = []
if trends["latency"].slope > 0.05: # 5% increase per week
warnings.append(
TrendWarning(
metric="latency",
trend="increasing",
current=trends["latency"].current,
projected=trends["latency"].project_forward(weeks=4),
severity="medium",
action="consider_optimization"
)
)
if trends["quality"].slope < -0.02: # 2% decrease per week
warnings.append(
TrendWarning(
metric="quality",
trend="degrading",
current=trends["quality"].current,
projected=trends["quality"].project_forward(weeks=4),
severity="high",
action="proactive_evolution_recommended"
)
)
return TrendAnalysis(
workflow_id=workflow_id,
trends=trends,
warnings=warnings,
cost=0.0 # Statistical analysis, no AI cost
)
Example:
Workflow: process_customer_order
Status: GRADUATED
Execution count: 456,231 (since graduation)
Trend Analysis (30-day window):
Latency trend:
- Current: 234ms average
- 30 days ago: 198ms average
- Slope: +1.2ms per day
- Projection (30 days): 270ms
- Severity: MEDIUM
- Cause: Gradual database growth (not a bug)
Quality trend:
- Current: 99.2% validation pass
- 30 days ago: 99.8% validation pass
- Slope: -0.02% per day
- Projection (30 days): 98.6%
- Severity: HIGH
- Cause: Input data quality degrading
Action recommended: PROACTIVE_EVOLUTION
Rationale:
The workflow is still within acceptable bounds NOW,
but trends suggest it will degrade significantly in
~30 days. Evolve now while we have time, rather than
wait for production incident.
Proposed evolution:
1. Add input sanitization layer
2. Optimize database queries (add index)
3. Implement caching for frequent reads
Estimated impact:
- Latency: 234ms → 180ms (23% faster)
- Quality: 99.2% → 99.9% (0.7% improvement)
- Resource cost: -15% (caching reduces DB load)
Cost: $0.085 (one-time evolution)
ROI: Prevents future incident, improves performance
This is the holy grail:
Fix problems before they become problems.
Traditional monitoring: Reactive (wait for failure, then fix)
Apprenticeship monitoring: Proactive (detect trends, fix before failure)
Here's another benefit nobody talks about:
Workflows can evolve to fit resource constraints.
class ResourceAwareEvolver:
async def optimize_for_resources(
self,
workflow_id: str,
constraint: ResourceConstraint
):
"""Evolve workflow to fit resource limits"""
current_usage = self.get_resource_usage(workflow_id)
if constraint.type == "MEMORY" and current_usage.memory > constraint.limit:
# Memory pressure - evolve to use less memory
analysis = await self.monitoring_ai.analyze_memory_usage(
workflow_id=workflow_id,
current_usage=current_usage.memory,
limit=constraint.limit
)
if analysis.recommendation == "STREAM_PROCESSING":
# Switch from batch to streaming
mutation = await self.generate_streaming_version(
workflow_id=workflow_id,
expected_memory_reduction=analysis.expected_savings
)
return mutation
elif constraint.type == "SCALE" and current_usage.instances < constraint.desired:
# Need more throughput - can we scale horizontally?
analysis = await self.monitoring_ai.analyze_scalability(
workflow_id=workflow_id,
current_instances=current_usage.instances,
desired_instances=constraint.desired
)
if analysis.bottleneck:
# Found a bottleneck preventing scale
mutation = await self.remove_bottleneck(
workflow_id=workflow_id,
bottleneck=analysis.bottleneck
)
return mutation
Example: Black Friday Traffic Spike
Event: Black Friday sale starting in 12 hours
Expected traffic: 10x normal
Current capacity: 5,000 orders/hour
Required capacity: 50,000 orders/hour
Workflow: process_customer_order (currently graduated)
Auto-scaling analysis:
Current: 10 instances handling 5,000 orders/hour (500 each)
Naive scale: 100 instances needed (10x)
Problem: Shared database bottleneck limits to 60 instances
Bottleneck detected:
- Database connection pool: Max 100 connections
- Current usage: 60/100 (10 instances × 6 connections each)
- Scaling to 100 instances would need 600 connections
- Current limit: 100
Solution: Reduce connections per instance
Mutation strategy:
1. Add connection pooling optimization
2. Implement read replicas for queries
3. Add caching layer for frequent lookups
4. Reduce per-instance connections: 6 → 2
Result:
- 100 instances × 2 connections = 200 connections
- Add 10 read replicas for queries
- 90% of queries hit cache
- Net database load: Actually DECREASES
New capacity:
- 100 instances × 500 orders/hour = 50,000 orders/hour
- Database load: LOWER than before
- Cost: One-time evolution ($0.125)
Mutation generated: process_customer_order v1.2.0
Status: TESTING (shadow mode)
Expected savings: Scale to 100x without database upgrade
ROI: Infinite (prevents $50K+ emergency database scaling)
The system detected a future scaling problem and fixed it BEFORE the traffic spike.
Traditional approach:
Apprenticeship approach:
Let's look at the concrete implementation:
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import numpy as np
from scipy import stats
@dataclass
class MetricDistribution:
"""Statistical distribution of a metric"""
mean: float
std_dev: float
median: float
percentile_95: float
percentile_99: float
samples: List[float] = field(default_factory=list)
def is_within_bounds(self, value: float, sigma: float = 3.0) -> bool:
"""Check if value is within N standard deviations"""
lower = self.mean - (sigma * self.std_dev)
upper = self.mean + (sigma * self.std_dev)
return lower <= value <= upper
def drift_score(self, value: float) -> float:
"""Calculate drift score (0.0 = perfect, 1.0 = extreme)"""
if self.std_dev == 0:
return 0.0 if value == self.mean else 1.0
# Z-score normalized to 0-1 range
z_score = abs((value - self.mean) / self.std_dev)
# Sigmoid to bound between 0 and 1
return 1.0 / (1.0 + np.exp(-z_score + 3))
@dataclass
class QualityProfile:
"""Learned quality profile for a workflow"""
workflow_id: str
tool_metrics: Dict[str, Dict[str, MetricDistribution]] = field(default_factory=dict)
execution_count: int = 0
graduated: bool = False
graduation_threshold: int = 50
def update(self, tool_name: str, metrics: Dict[str, float]):
"""Update profile with new execution metrics"""
if tool_name not in self.tool_metrics:
self.tool_metrics[tool_name] = {}
for metric_name, value in metrics.items():
if metric_name not in self.tool_metrics[tool_name]:
self.tool_metrics[tool_name][metric_name] = MetricDistribution(
mean=value,
std_dev=0.0,
median=value,
percentile_95=value,
percentile_99=value,
samples=[value]
)
else:
# Update distribution
dist = self.tool_metrics[tool_name][metric_name]
dist.samples.append(value)
# Recalculate statistics
dist.mean = np.mean(dist.samples)
dist.std_dev = np.std(dist.samples)
dist.median = np.median(dist.samples)
dist.percentile_95 = np.percentile(dist.samples, 95)
dist.percentile_99 = np.percentile(dist.samples, 99)
self.execution_count += 1
# Check for graduation
if not self.graduated and self.execution_count >= self.graduation_threshold:
self.graduated = True
def check_drift(self, tool_name: str, metrics: Dict[str, float]) -> Dict[str, float]:
"""Check for drift in metrics (returns drift scores)"""
if tool_name not in self.tool_metrics:
return {} # No profile yet
drift_scores = {}
for metric_name, value in metrics.items():
if metric_name in self.tool_metrics[tool_name]:
dist = self.tool_metrics[tool_name][metric_name]
drift_scores[metric_name] = dist.drift_score(value)
return drift_scores
from enum import Enum
from typing import Optional
class MonitoringTier(Enum):
FULL = "full" # Apprentice mode - expensive
STATISTICAL = "statistical" # Graduate mode - free
DRIFT_INVESTIGATION = "drift" # Re-engaged monitoring
class MonitoringManager:
def __init__(self):
self.fast_model = "gemma2:2b"
self.medium_model = "llama3:8b"
self.expensive_model = "claude-3.5-sonnet"
async def monitor_execution(
self,
tier: MonitoringTier,
tool_name: str,
metrics: Dict[str, float],
quality_profile: Optional[QualityProfile] = None
) -> MonitoringResult:
"""Route monitoring based on tier"""
if tier == MonitoringTier.FULL:
# Apprentice mode - learn everything
return await self.full_monitoring(tool_name, metrics, quality_profile)
elif tier == MonitoringTier.STATISTICAL:
# Graduate mode - just check drift
if quality_profile is None:
raise ValueError("Quality profile required for statistical monitoring")
drift_scores = quality_profile.check_drift(tool_name, metrics)
max_drift = max(drift_scores.values()) if drift_scores else 0.0
if max_drift > 0.15: # Drift threshold
# Escalate to drift investigation
return await self.investigate_drift(
tool_name, metrics, quality_profile, drift_scores
)
else:
# All good, no AI cost
return MonitoringResult(
status="OK",
tier="statistical",
drift_scores=drift_scores,
cost=0.0
)
elif tier == MonitoringTier.DRIFT_INVESTIGATION:
# Drift detected - investigate
return await self.investigate_drift(
tool_name, metrics, quality_profile, {}
)
async def full_monitoring(
self,
tool_name: str,
metrics: Dict[str, float],
quality_profile: Optional[QualityProfile]
) -> MonitoringResult:
"""Full AI-powered monitoring (expensive)"""
# Tier 1: Fast check
fast_result = await self.fast_check(tool_name, metrics, quality_profile)
if fast_result.confidence > 0.95:
return MonitoringResult(
status=fast_result.status,
tier="fast",
confidence=fast_result.confidence,
cost=0.001
)
# Tier 2: Medium analysis
medium_result = await self.medium_check(tool_name, metrics, quality_profile)
if medium_result.confidence > 0.90:
return MonitoringResult(
status=medium_result.status,
tier="medium",
confidence=medium_result.confidence,
analysis=medium_result.analysis,
cost=0.016
)
# Tier 3: Expensive investigation
expensive_result = await self.expensive_check(tool_name, metrics, quality_profile)
return MonitoringResult(
status=expensive_result.status,
tier="expensive",
confidence=expensive_result.confidence,
analysis=expensive_result.analysis,
recommendations=expensive_result.recommendations,
cost=0.125
)
class GraduationController:
def __init__(self):
self.monitoring_manager = MonitoringManager()
self.profiles: Dict[str, QualityProfile] = {}
async def execute_workflow(
self,
workflow_id: str,
workflow_fn: Callable,
*args,
**kwargs
):
"""Execute workflow with appropriate monitoring tier"""
# Get or create quality profile
if workflow_id not in self.profiles:
self.profiles[workflow_id] = QualityProfile(
workflow_id=workflow_id,
graduation_threshold=50 # Configurable
)
profile = self.profiles[workflow_id]
# Determine monitoring tier
if not profile.graduated:
tier = MonitoringTier.FULL
else:
tier = MonitoringTier.STATISTICAL
# Execute with monitoring
result = await self.monitored_execution(
workflow_id=workflow_id,
workflow_fn=workflow_fn,
tier=tier,
profile=profile,
args=args,
kwargs=kwargs
)
return result
async def monitored_execution(
self,
workflow_id: str,
workflow_fn: Callable,
tier: MonitoringTier,
profile: QualityProfile,
args: tuple,
kwargs: dict
):
"""Execute workflow with instrumentation"""
# Capture baseline
start_time = time.time()
start_memory = self.get_memory_usage()
# Execute workflow
try:
result = await workflow_fn(*args, **kwargs)
status = "success"
except Exception as e:
result = None
status = "error"
error = e
# Capture metrics
execution_time = time.time() - start_time
memory_used = self.get_memory_usage() - start_memory
metrics = {
"execution_time": execution_time,
"memory_used": memory_used,
"status": status
}
# Monitor based on tier
monitoring_result = await self.monitoring_manager.monitor_execution(
tier=tier,
tool_name=workflow_id,
metrics=metrics,
quality_profile=profile
)
# Update profile
if tier == MonitoringTier.FULL:
profile.update(workflow_id, metrics)
if profile.graduated:
logger.info(
f"Workflow {workflow_id} GRADUATED after "
f"{profile.execution_count} successful executions"
)
# Handle drift if detected
if monitoring_result.status == "DRIFT":
await self.handle_drift(workflow_id, monitoring_result, profile)
return WorkflowResult(
result=result,
metrics=metrics,
monitoring=monitoring_result,
profile=profile
)
Everything I've described here is implementable with current technology:
The hard part isn't the technology.
The hard part is changing how we think about monitoring.
From: "Monitor everything, always" To: "Learn once, monitor only when needed"
From: "Reactive incident response" To: "Proactive trend-based evolution"
From: "Static quality thresholds" To: "Learned quality profiles"
Imagine this running at planetary scale:
Company: Global e-commerce platform
Workflows: 10,000+ unique workflows
Total executions: 100M per day
Traditional monitoring cost:
100M executions × $0.01 = $1M/day
Annual: $365M
Apprenticeship approach:
New workflows per day: ~10
Apprenticeship cost: 10 × $1.60 = $16/day
Drift investigations: ~50/day × $0.05 = $2.50/day
Major evolutions: ~5/day × $0.15 = $0.75/day
Daily cost: $19.25
Annual: $7,026
Savings: $364,992,974 per year
ROI: 51,971:1
But the savings are actually the least interesting part.
The interesting part is what becomes possible:
Every workflow continuously improves based on trends:
Without human intervention.
Failed mutations propagate as avoidance rules:
Across all workflows, forever.
Trend analysis predicts problems weeks in advance:
Move from reactive to proactive.
Graduated workflows can be evolved without risk:
Continuous delivery for AI workflows.
The Apprenticeship Pattern is simple:
It's not revolutionary. It's obvious in hindsight.
But we're not doing it.
Instead, we're paying monitoring AI to watch perfect executions, missing actual problems, and responding reactively when things break.
The Apprenticeship Pattern inverts this:
The result:
This is how workflows should work.
Apprentice → Graduate → Expert → Teacher
Just like humans.
Except the training wheels know when to come off by themselves.
Want to implement the Apprenticeship Pattern in your workflows?
Repository: https://github.com/scottgal/mostlylucid.dse
Key Files:
src/quality_profile.py - Quality profile learningsrc/monitoring_manager.py - Tiered monitoringsrc/graduation_controller.py - Apprenticeship orchestrationsrc/drift_detector.py - Statistical drift detectionsrc/proactive_evolver.py - Trend-based evolutionExample:
from src import GraduationController
controller = GraduationController()
# Define your workflow
async def my_workflow(input_data):
# Your workflow logic here
result = await process_data(input_data)
return result
# Execute with automatic graduation
result = await controller.execute_workflow(
workflow_id="my_workflow_v1",
workflow_fn=my_workflow,
input_data={"foo": "bar"}
)
print(f"Status: {result.monitoring.status}")
print(f"Cost: ${result.monitoring.cost:.4f}")
print(f"Graduated: {result.profile.graduated}")
First 50 executions: Full monitoring, learning profile After graduation: Free statistical monitoring On drift: Auto-investigate and evolve
This is Part 2 of the "Cooking with DiSE" series, exploring practical patterns for production-scale workflow evolution. The Apprenticeship Pattern demonstrates how workflows can learn to run independently, saving millions in monitoring costs while actually improving quality through proactive evolution.
© 2025 Scott Galloway — Unlicense — All content and source code on this site is free to use, copy, modify, and sell.