02. Routing Policies

Routing policies constitute the decision framework that determines which backend receives each incoming request. These policies encode business logic, technical constraints, and operational priorities into executable routing infrastructure. Effective policies balance multiple competing objectives while remaining maintainable as requirements evolve.

Policy classification begins with the distinction between static and dynamic routing. Static policies apply fixed rules that never change based on runtime conditions. A policy that routes all medical records to local inference exemplifies static routing. Dynamic policies evaluate runtime signals such as queue depth, error rates, or token budgets before committing a route. A policy that routes to cloud when local queue exceeds fifty pending requests represents dynamic routing.

Content-aware policies inspect request payload to extract metadata that influences routing decisions. Keyword detection flags sensitive terms. Schema validation confirms expected field structures. Classification models predict task complexity and select appropriately sized models. This inspection adds latency but enables sophisticated routing that fixed rules cannot express.

Priority policies establish hierarchies when multiple applicable rules exist. Explicit priority numbers resolve conflicts. First-match-wins semantics simplify debugging. Weighted random selection distributes traffic across acceptable backends proportionally. The chosen priority model shapes operational complexity significantly.

Fallback policies address backend unavailability. Primary routes specify secondary alternatives. Circuit breaker patterns disable failing backends temporarily. Retry budgets apply before escalation. Graceful degradation ensures requests receive service even when preferred backends reject load.

Policy administration requires change management discipline. Configuration drift where production differs from staging creates risk. Feature flags enable gradual rollout of policy modifications. A/B testing frameworks validate policy changes against baseline metrics before full deployment. Version-controlled policy definitions enable rollback when regressions occur.

Metrics inform policy refinement. Per-backend latency histograms reveal performance degradation. Token consumption tracking forecasts capacity. Error rate alerts surface infrastructure problems before user impact. Continual measurement creates feedback loops that improve routing decisions over time.

class RoutingPolicy(ABC):
    """Base class defining routing policy interface."""
    
    def __init__(self, priority: int = 100):
        self.priority = priority
        self.metrics = PolicyMetrics()
    
    @abstractmethod
    async def evaluate(self, request: Request) -> RouteDestination:
        """Evaluate request and return routing destination."""
        pass
    
    async def record_outcome(self, destination: RouteDestination, 
                             latency_ms: float, success: bool):
        """Record routing outcome for metrics collection."""
        self.metrics.record(destination, latency_ms, success)