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Cross-Domain Agents

Design agents that transfer knowledge across different domains

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Building Cross-Domain Systems

A production cross-domain agent system needs four components: transfer engine (handles knowledge transfer), domain adapters (efficient per-domain layers), evaluator (measures effectiveness), and multi-domain pipeline (orchestrates deployment). Here's production-ready code.

Interactive: Implementation Explorer

Explore each system component:

🔄
Transfer Engine
Core transfer logic for cross-domain agents
class DomainTransferEngine:
    """Transfer learning engine for cross-domain agents"""
    
    def __init__(self, source_model: Model, source_domain: str):
        self.source_model = source_model
        self.source_domain = source_domain
        self.adaptation_history = []
        
    def transfer_to_domain(
        self,
        target_domain: str,
        target_data: Dataset,
        strategy: str = 'parameter'
    ) -> AdaptedModel:
        """Transfer source model to new domain"""
        
        # 1. Analyze domain gap
        gap_analysis = self._analyze_domain_gap(
            self.source_domain, 
            target_domain,
            target_data
        )
        
        print(f"Domain gap: {gap_analysis.similarity}%")
        
        # 2. Select transfer strategy based on gap
        if strategy == 'auto':
            strategy = self._select_strategy(gap_analysis)
        
        # 3. Execute transfer
        if strategy == 'feature':
            adapted_model = self._feature_transfer(target_data)
        elif strategy == 'parameter':
            adapted_model = self._parameter_transfer(target_data)
        elif strategy == 'architecture':
            adapted_model = self._architecture_transfer(target_data)
        else:
            adapted_model = self._knowledge_distillation(target_data)
        
        # 4. Record transfer
        self.adaptation_history.append({
            'source': self.source_domain,
            'target': target_domain,
            'strategy': strategy,
            'gap': gap_analysis.similarity,
            'accuracy': adapted_model.evaluate(target_data.test)
        })
        
        return adapted_model
    
    def _analyze_domain_gap(
        self,
        source: str,
        target: str,
        data: Dataset
    ) -> DomainGap:
        """Measure similarity between domains"""
        
        # Extract features from both domains
        source_features = self.source_model.encode(data.source_samples)
        target_features = self.source_model.encode(data.target_samples)
        
        # Calculate distribution distance
        mmd_distance = maximum_mean_discrepancy(
            source_features,
            target_features
        )
        
        # Convert to similarity percentage
        similarity = max(0, 100 - (mmd_distance * 100))
        
        return DomainGap(
            similarity=similarity,
            distance=mmd_distance,
            recommendation=self._get_recommendation(similarity)
        )
    
    def _parameter_transfer(self, target_data: Dataset) -> Model:
        """Fine-tune all parameters on target domain"""
        
        # Clone source model
        model = self.source_model.clone()
        
        # Use low learning rate to preserve knowledge
        optimizer = Adam(
            model.parameters(),
            lr=1e-5  # 10x lower than original training
        )
        
        # Fine-tune
        for epoch in range(10):
            for batch in target_data.train:
                loss = model.compute_loss(batch)
                loss.backward()
                optimizer.step()
                optimizer.zero_grad()
        
        return model

Complete Usage Example

# Initialize with base model trained on large corpus
base_model = load_pretrained_model("gpt-4-base")
pipeline = MultiDomainPipeline(base_model)

# Deploy to medical domain
medical_result = pipeline.deploy_to_domain(
    domain_name="medical_diagnosis",
    training_data=load_medical_data(),
    validation_data=load_medical_validation(),
    min_accuracy=0.85
)

# Deploy to legal domain
legal_result = pipeline.deploy_to_domain(
    domain_name="legal_analysis",
    training_data=load_legal_data(),
    validation_data=load_legal_validation(),
    min_accuracy=0.80
)

# Deploy to manufacturing domain
manufacturing_result = pipeline.deploy_to_domain(
    domain_name="quality_control",
    training_data=load_manufacturing_data(),
    validation_data=load_manufacturing_validation(),
    min_accuracy=0.90
)

print(f"Medical: {medical_result.accuracy:.2%}")
print(f"Legal: {legal_result.accuracy:.2%}")
print(f"Manufacturing: {manufacturing_result.accuracy:.2%}")

# Route requests to appropriate domain agents
request = Request(
    text="Patient presents with chest pain...",
    metadata={"source": "ehr_system"}
)

response = pipeline.route_request(request)
print(f"Domain: {response.domain}")
print(f"Confidence: {response.confidence:.2%}")

# Monitor all domains
stats = pipeline.monitor_performance()
for domain, metrics in stats.items():
    print(f"{domain}: {metrics['accuracy']:.2%} accuracy, {metrics['avg_latency']:.0f}ms latency")
💡
Production Deployment Strategy

Phase 1 (Week 1-2): Deploy base model, measure baseline performance across target domains.
Phase 2 (Week 3-4): Transfer to highest-priority domain, validate with human review.
Phase 3 (Month 2): Scale to 3-5 domains using adapters, monitor for catastrophic forgetting.
Phase 4 (Month 3+): Continuous learning - retrain base model monthly incorporating all domain data.

Success metrics: 80%+ accuracy across all domains, <100ms latency, <1% catastrophic forgetting on source domains.

← Domain Adaptation