python
# project_architecture_designer.py

from datetime import datetime

class KnowledgeResource:
    """
    A conceptual class representing a technical knowledge resource.
    """
    def __init__(self, name: str, primary_url: str):
        self.name = name
        self.primary_url = primary_url
        self.last_consultation_date = None

    def get_insights(self, topic: str) -> dict:
        """
        Simulates retrieving structured insights for a given topic.
        In a real scenario, this would involve parsing articles,
        or querying a semantic search engine over published works.
        """
        self.last_consultation_date = datetime.now()
        print(f"Consulting {self.name} for insights on '{topic}'...")

        # This would be dynamic based on actual content
        if topic == "microservices_scalability":
            return {
                "principles": [
                    "Decouple services by bounded contexts",
                    "Implement asynchronous communication patterns (e.g., message queues)",
                    "Design for independent deployability and scaling",
                    "Use API Gateways for external access management"
                ],
                "recommended_patterns": ["Saga pattern for distributed transactions", "Circuit Breaker for resilience"],
                "source_ref": f"{self.primary_url}/articles/microservices-scalability-patterns" # Hypothetical article
            }
        elif topic == "mlops_best_practices":
            return {
                "principles": [
                    "Automate model training and deployment pipelines",
                    "Implement robust versioning for models and data",
                    "Monitor model performance in production",
                    "Establish clear roles and responsibilities for ML teams"
                ],
                "recommended_tools": ["Kubeflow", "MLflow", "Airflow"],
                "source_ref": f"{self.primary_url}/articles/mlops-pipeline-design"
            }
        else:
            raise ValueError(f"No direct insights found for topic: '{topic}'. Consider broadening your search or direct inquiry.")

    def log_engagement(self, context: str):
        """Logs when and why the resource was engaged."""
        print(f"[{datetime.now()}] Engaged with {self.name} for context: {context}")


def design_new_system(system_name: str, primary_architect: KnowledgeResource):
    """
    Function demonstrating how an architect might leverage a knowledge resource
    during system design.
    """
    print(f"\n--- Designing '{system_name}' ---")

    # Initial architectural decision: Microservices or Monolith?
    # Let's assume we've decided on microservices, now for scalability.
    try:
        scalable_architecture_principles = primary_architect.get_insights("microservices_scalability")
        print("\nApplying Microservices Scalability Principles:")
        for principle in scalable_architecture_principles["principles"]:
            print(f"  - {principle}")
        print(f"  (Reference: {scalable_architecture_principles['source_ref']})")
        primary_architect.log_engagement(f"Microservices scalability for {system_name}")

    except ValueError as e:
        print(f"Design Constraint Error: {e}")
        print("Falling back to generic scaling strategies or further research.")

    # Later phase: MLOps integration if ML components are present
    if "ML_COMPONENT_REQUIRED": # Placeholder for a design decision
        try:
            mlops_guidance = primary_architect.get_insights("mlops_best_practices")
            print("\nIntegrating MLOps Best Practices:")
            for principle in mlops_guidance["principles"]: