🔮 Predictive Quality Oracle - AI Quality Before Deployment

🔮 Predictive Quality Oracle

Vision: Predict agent quality issues BEFORE they happen in production. Use AI to forecast performance, detect drift, and prevent failures.

Beyond Reactive Evaluation

Traditional: Test after deployment, fix when broken
Our innovation: Predict quality issues before deployment, prevent problems proactively

Predictive Systems

1. Quality Forecasting Engine

class QualityOracle {
  async predictQualityScore(
    agent: AgentDNA,
    targetEnvironment: Environment
  ): Promise<QualityPrediction> {
    // Historical performance analysis
    const historical = await this.analyzeLineage(agent)
    
    // Simulation-based testing
    const simulated = await this.simulateInEnvironment(
      agent,
      targetEnvironment,
      { scenarios: 1000 }
    )
    
    // ML-based prediction
    const predicted = await this.mlPredict({
      agent_dna: agent.genome,
      environment: targetEnvironment,
      historical_data: historical,
      simulation_results: simulated
    })
    
    return {
      predicted_score: predicted.score,
      confidence: predicted.confidence,
      risk_factors: predicted.risks,
      failure_probability: predicted.failure_prob,
      recommended_actions: predicted.recommendations
    }
  }
}

2. Drift Detection Before It Happens

class DriftPredictor {
  async predictDrift(agent: Agent): Promise<DriftPrediction> {
    // Analyze trend lines
    const trends = await this.analyzeTrends(agent, {
      metrics: ['accuracy', 'latency', 'cost'],
      window: '30d'
    })
    
    // Time series forecasting
    const forecast = await this.forecast(trends, {
      horizon: '7d',
      confidence_interval: 0.95
    })
    
    // Detect anomaly patterns forming
    const anomalyRisk = await this.detectEmergingAnomalies(
      agent.recent_behavior
    )
    
    return {
      drift_probability: forecast.drift_prob,
      expected_drift_date: forecast.expected_date,
      severity: this.calculateSeverity(forecast),
      mitigation: this.recommendMitigation(forecast),
      early_warning_triggers: anomalyRisk.triggers
    }
  }
}

3. Canary Prediction System

canary_prediction:
  agent: agent.sales.v43
  deployment_plan:
    stage_1:
      traffic: 5%
      duration: 1h
      predicted_outcomes:
        success_rate: 0.92 ± 0.03
        avg_latency: 245ms ± 50ms
        error_rate: 0.008 ± 0.002
      confidence: 0.87
      recommendation: PROCEED
      
    stage_2:
      traffic: 25%
      duration: 4h
      predicted_outcomes:
        success_rate: 0.91 ± 0.04
        avg_latency: 280ms ± 60ms
        error_rate: 0.012 ± 0.003
      confidence: 0.82
      risk_factors:
        - increased_load_may_affect_latency
        - edge_cases_in_segment_B
      recommendation: PROCEED_WITH_CAUTION
      
    stage_3:
      traffic: 100%
      predicted_outcomes:
        success_rate: 0.89 ± 0.05
        avg_latency: 320ms ± 80ms
        error_rate: 0.018 ± 0.005
      confidence: 0.75
      risk_factors:
        - performance_degradation_at_scale
        - possible_context_overflow
      recommendation: REVIEW_BEFORE_PROCEED
      suggested_actions:
        - increase_context_window_limit
        - add_caching_layer
        - consider_horizontal_scaling

AI-Powered Quality Analysis

1. Behavioral Modeling

// Build behavioral model of agent
class BehaviorModeler {
  async buildModel(agent: Agent): Promise<BehaviorModel> {
    // Learn from historical interactions
    const interactions = await this.getInteractions(agent, {
      limit: 10000
    })
    
    // Train behavioral model
    const model = await this.trainModel(interactions, {
      architecture: 'transformer',
      objective: 'predict_next_action'
    })
    
    // Validate model accuracy
    const validation = await this.validate(model)
    
    return {
      model: model,
      accuracy: validation.accuracy,
      can_predict: validation.can_predict,
      typical_behaviors: this.extractBehaviors(model),
      anomaly_detector: this.buildAnomalyDetector(model)
    }
  }
  
  async detectBehavioralAnomaly(
    agent: Agent,
    interaction: Interaction
  ): Promise<AnomalyReport> {
    const model = await this.getModel(agent)
    
    // Compare actual vs expected behavior
    const expected = await model.predict(interaction.context)
    const actual = interaction.action
    
    const divergence = this.calculateDivergence(expected, actual)
    
    if (divergence > ANOMALY_THRESHOLD) {
      return {
        is_anomaly: true,
        divergence_score: divergence,
        expected_behavior: expected,
        actual_behavior: actual,
        possible_causes: this.diagnoseCauses(divergence),
        severity: this.assessSeverity(divergence)
      }
    }
  }
}

2. Counterfactual Analysis

// "What if" analysis for quality
class CounterfactualAnalyzer {
  async analyzeWhatIf(
    agent: Agent,
    changes: ConfigChanges
  ): Promise<ImpactAnalysis> {
    // Create virtual clone with changes
    const virtualAgent = this.applyChanges(agent, changes)
    
    // Simulate in historical scenarios
    const scenarios = await this.getHistoricalScenarios({
      limit: 1000,
      representative: true
    })
    
    const results = await Promise.all(
      scenarios.map(scenario => 
        this.simulate(virtualAgent, scenario)
      )
    )
    
    // Compare to actual historical performance
    const comparison = this.compareToBaseline(
      results,
      agent.historical_performance
    )
    
    return {
      predicted_impact: comparison.impact,
      confidence: comparison.confidence,
      improvement_probability: comparison.improvement_prob,
      risk_of_degradation: comparison.degradation_prob,
      key_insights: comparison.insights,
      recommendation: this.makeRecommendation(comparison)
    }
  }
}

3. Root Cause Prediction

// Predict root causes before failures happen
class RootCausePredictor {
  async predictRootCause(
    symptom: Symptom
  ): Promise<RootCausePrediction> {
    // Build causal graph from historical data
    const causalGraph = await this.buildCausalGraph()
    
    // Trace symptom back to potential causes
    const potentialCauses = causalGraph.traceCauses(symptom)
    
    // Rank by likelihood
    const rankedCauses = await this.rankByLikelihood(
      potentialCauses,
      {
        current_context: symptom.context,
        historical_patterns: this.patterns
      }
    )
    
    return {
      most_likely_cause: rankedCauses[0],
      all_potential_causes: rankedCauses,
      confidence: this.calculateConfidence(rankedCauses),
      prevention_strategies: this.suggestPrevention(rankedCauses),
      monitoring_recommendations: this.suggestMonitoring(rankedCauses)
    }
  }
}

Integration with Your Stack

OSSA Extension

# schemas/v0.1.9/predictive-quality.yaml
paths:
  /quality/predict:
    post:
      description: Predict quality before deployment
      requestBody:
        content:
          application/json:
            schema:
              properties:
                agent_dna: object
                target_environment: string
                prediction_horizon: string
                
  /quality/drift/predict:
    get:
      description: Predict future drift
      
  /quality/whatif:
    post:
      description: Counterfactual analysis
      
  /quality/rootcause/predict:
    post:
      description: Predict root cause from symptoms

Buildkit Commands

# Predictive analysis
buildkit quality predict --agent agent-001 --environment prod
buildkit quality drift-forecast --agent agent-001 --horizon 7d
buildkit quality whatif --agent agent-001 --change 'temperature=0.9'
buildkit quality canary-simulate --agent agent-001 --traffic-plan gradual

# Root cause analysis
buildkit quality diagnose --symptom latency_increase
buildkit quality rootcause --predict --from-symptoms

# Behavioral analysis
buildkit quality behavior-model --agent agent-001
buildkit quality detect-anomaly --agent agent-001 --realtime

Studio-UI Dashboard

Predictive Quality Center:

• Quality forecasting graphs (7, 14, 30 day)
• Drift probability heat maps
• Canary stage predictions
• What-if scenario comparisons
• Root cause probability trees
• Behavioral anomaly alerts

Risk Assessment:

• Real-time risk scores
• Failure probability trends
• Early warning indicators
• Mitigation recommendations
• Confidence intervals

Advanced Features

1. Ensemble Predictions

// Combine multiple prediction methods
class EnsemblePredictor {
  async predict(agent: Agent): Promise<Prediction> {
    const predictions = await Promise.all([
      this.statisticalPredict(agent),    // Time series
      this.mlPredict(agent),             // Neural network
      this.simulationPredict(agent),     // Monte Carlo
      this.expertSystemPredict(agent),   // Rule-based
      this.causalPredict(agent)          // Causal inference
    ])
    
    // Weight by historical accuracy
    return this.weightedEnsemble(predictions, {
      weights: this.adaptiveWeights()
    })
  }
}

2. Continuous Learning

// Prediction models improve over time
class ContinuousLearning {
  async updateModels() {
    // Compare predictions to actual outcomes
    const accuracy = await this.measurePredictionAccuracy()
    
    // Retrain models with new data
    await this.retrainModels({
      data: this.recentOutcomes,
      focus_on_errors: true
    })
    
    // Adjust ensemble weights
    await this.adjustWeights({
      based_on: accuracy
    })
  }
}

3. Explainable Predictions

// Explain WHY a prediction was made
class ExplainablePredictor {
  async explain(prediction: Prediction): Promise<Explanation> {
    return {
      primary_factors: [
        {
          factor: 'Recent latency trend',
          contribution: 0.35,
          evidence: 'Latency increased 15% over 7 days'
        },
        {
          factor: 'Similar agent history',
          contribution: 0.28,
          evidence: 'Agent v41 had same pattern before failure'
        },
        {
          factor: 'Load pattern change',
          contribution: 0.22,
          evidence: 'Traffic pattern shifted to edge cases'
        }
      ],
      confidence_factors: {
        historical_precedent: 0.85,
        data_quality: 0.90,
        model_agreement: 0.75
      },
      counterfactuals: [
        'If load stayed constant, failure prob would be 12% (vs 34%)',
        'If context window increased, latency risk drops 40%'
      ]
    }
  }
}

Integration Points

Agent-Buildkit

• Pre-deployment quality checks
• Automatic rollback triggers
• Configuration recommendations

Agent-Tracer

• Real-time behavioral monitoring
• Anomaly detection alerts
• Drift early warnings

Workflow-Engine

• Automated quality gates
• Prediction-based deployment decisions
• Rollback automation

Compliance-Engine (#3)

• Evaluation prediction
• Performance forecasting
• Risk assessment

Compliance & Safety

Prediction Accuracy Monitoring

accuracy_tracking:
  prediction_vs_actual:
    tracked: true
    alert_if_below: 0.75
  false_positive_rate:
    acceptable: 0.10
    current: 0.08
  false_negative_rate:
    acceptable: 0.05  # More critical
    current: 0.03

Ethical AI

• Explain all predictions
• No black-box decisions
• Human oversight for critical predictions
• Bias detection in predictions

Success Metrics

• 80%+ prediction accuracy
• 95% of failures predicted 24h in advance
• 50% reduction in production incidents
• 90% confidence in go/no-go decisions
• <5% false positive rate
• Zero critical missed predictions

Implementation Timeline

Week 1-3: Quality forecasting engine
Week 4-6: Drift prediction system
Week 7-9: Behavioral modeling
Week 10-12: Counterfactual analysis
Week 13-15: Root cause prediction
Week 16-18: Ensemble & continuous learning
Week 19-20: Studio-UI dashboards

Related Issues

• #3 (Continuous Evaluation - foundation)
• agent-buildkit #24 (Agent Forge)
• agent-buildkit #28 (Agent DNA)
• agent-brain #13 (Collective Consciousness)
• agent-tracer #6 (Observability)

Priority: Critical - Prevent production issues
Innovation Level: 🔮 Breakthrough
Status: Design phase

This gives you precognitive quality assurance! 🔮✨