Agent Best Practices

class QualityController:
    def __init__(self, quality_threshold=0.95):
        self.threshold = quality_threshold
        self.quality_history = []
        
    async def validate_output(self, task, output):
        # Multi-dimensional quality check
        scores = {
            'accuracy': await self.check_accuracy(task, output),
            'relevance': await self.check_relevance(task, output),
            'completeness': await self.check_completeness(task, output),
            'format': await self.check_format(output)
        }
        
        overall_quality = sum(scores.values()) / len(scores)
        
        # Track quality over time
        self.quality_history.append(overall_quality)
        
        # Reject if below threshold
        if overall_quality < self.threshold:
            raise QualityError(f"Output quality {overall_quality:.3f} below threshold")
            
        return overall_quality, scores

import torch
from torch.utils.data import DataLoader

class OptimizedAgent:
    def __init__(self):
        # Enable mixed precision for faster inference
        self.scaler = torch.cuda.amp.GradScaler()
        
        # Optimize model for inference
        self.model = torch.jit.script(self.model)
        self.model.eval()
        
    @torch.inference_mode()
    async def process_batch(self, tasks):
        # Batch processing for efficiency
        inputs = [task.input for task in tasks]
        
        with torch.cuda.amp.autocast():
            outputs = self.model(inputs)
            
        return outputs

class MemoryOptimizedAgent:
    def __init__(self, max_memory_gb=8):
        self.max_memory = max_memory_gb * 1024**3
        self.current_memory = 0
        
    async def process_with_memory_limit(self, task):
        # Check available memory
        if self.current_memory > self.max_memory * 0.8:
            await self.cleanup_cache()
            
        # Process with memory tracking
        try:
            result = await self.model.process(task.input)
            return result
        finally:
            # Cleanup after processing
            torch.cuda.empty_cache()

import aiohttp
import asyncio

class NetworkOptimizedAgent:
    def __init__(self):
        # Persistent connection pool
        self.session = aiohttp.ClientSession(
            connector=aiohttp.TCPConnector(
                limit=100,
                limit_per_host=20,
                keepalive_timeout=30
            )
        )
        
    async def efficient_api_call(self, endpoint, data):
        async with self.session.post(endpoint, json=data) as response:
            return await response.json()

class BlueGreenDeployment:
    def __init__(self):
        self.blue_instance = None
        self.green_instance = None
        self.active_color = 'blue'
        
    async def deploy_new_version(self, new_model):
        inactive_color = 'green' if self.active_color == 'blue' else 'blue'
        
        # Deploy to inactive instance
        if inactive_color == 'green':
            self.green_instance = await self.create_instance(new_model)
        else:
            self.blue_instance = await self.create_instance(new_model)
            
        # Health check new instance
        if await self.health_check(inactive_color):
            # Switch traffic
            self.active_color = inactive_color
            print(f"Switched to {self.active_color} deployment")
        else:
            raise DeploymentError("New instance failed health checks")

class RollingUpdate:
    def __init__(self, instances: List[AgentInstance]):
        self.instances = instances
        
    async def update_gradually(self, new_model):
        for i, instance in enumerate(self.instances):
            print(f"Updating instance {i+1}/{len(self.instances)}")
            
            # Remove from load balancer
            await self.remove_from_lb(instance)
            
            # Update instance
            await instance.update_model(new_model)
            
            # Health check
            if await instance.health_check():
                # Add back to load balancer
                await self.add_to_lb(instance)
            else:
                # Rollback on failure
                await instance.rollback()
                await self.add_to_lb(instance)
                raise UpdateError(f"Failed to update instance {i}")