Test chunk

'query']) complexity = self.assess_complexity(request, intent) if complexity == 'simple': return RoutingDecision(ModelTier.SMALL, "claude-haiku-4-5", 0.0004, "Simple query") elif complexity == 'medium': return RoutingDecision(ModelTier.MEDIUM, "claude-sonnet-4", 0.003, "Medium complexity") elif complexity == 'complex': return RoutingDecision(ModelTier.LARGE, "claude-opus-4", 0.015, "Complex reasoning") else: return RoutingDecision(ModelTier.SMALL, "claude-haiku-4-5", 0.0004, "Escalation chain")

4.3 Cascading Execution and Auto-Escalation

class CascadingExecutor:
    async def execute_with_fallback(self, request):
        chain = [
            (ModelTier.SMALL, "claude-haiku-4-5", 1024),
            (ModelTier.MEDIUM, "claude-sonnet-4", 2048),
            (ModelTier.LARGE, "claude-opus-4", 4096),
        ]

        total_cost = 0
        for tier, model, max_tokens in chain:
            response = await self.call_model(model, request, max_tokens)
            total_cost += response.cost
            quality = await self.evaluate_quality(response, request)
            if quality.score >= 0.8:
                return CascadingResult(response, model, tier, total_cost, quality.score)
            request['context'] = "Previous attempt was insufficient. Please improve."
            request['original_response'] = response.text[:200]

        return CascadingResult(response, model, tier, total_cost, quality.score)

5. Inference Layer Cost Optimization

5.1 Inference Request Batching

class InferenceBatcher:
    def __init__(self, model_adapter, max_batch_size=32, max_wait_ms=50):
        self.model = model_adapter
        self.max_batch_size = max_batch_size
        self.max_wait_ms = max_wait_ms
        self.pending_requests = []

    async def submit(self, request):