17. Scaling Agents

Scaling multi-agent systems involves balancing throughput, latency, and resource costs. Unlike stateless services, agent scaling must account for stateful execution contexts and variable computational demands.

Horizontal Scaling Patterns

Stateless orchestration components scale horizontally without coordination challenges. Agent workers, however, require careful placement to optimize cache utilization and minimize context transfer overhead.

Stateless Orchestrator Scaling: Multiple orchestration instances share load via standard load balancers. State lives in external persistence layer.

Agent Pool Management: Pools of agent workers maintain warm instances ready to accept tasks. Dynamic sizing adjusts pool size based on queue depth.

# scaling/agent_pool.py
from dataclasses import dataclass
from typing import Optional
import asyncio
from datetime import datetime

@dataclass
class AgentInstance:
    instance_id: str
    agent_type: str
    status: str  # "ready", "busy", "cooldown"
    current_task: Optional[str] = None
    last_used: Optional[datetime] = None
    context_size: int = 0

class AgentPoolManager:
    def __init__(
        self, 
        min_size: int = 2,
        max_size: int = 10,
        scale_up_threshold: float = 0.7,
        scale_down_threshold: float = 0.2
    ):
        self.min_size = min_size
        self.max_size = max_size
        self.scale_up_threshold = scale_up_threshold
        self.scale_down_threshold = scale_down_threshold
        self.instances: list[AgentInstance] = []
        self.queue_depth: int = 0
    
    def get_available_instance(self) -> Optional[AgentInstance]:
        ready = [i for i in self.instances if i.status == "ready"]
        return ready[0] if ready else None
    
    def register_instance(self, instance: AgentInstance):
        self.instances.append(instance)
    
    def release_instance(self, instance_id: str):
        for inst in self.instances:
            if inst.instance_id == instance_id:
                inst.status = "ready"
                inst.current_task = None
    
    def should_scale_up(self) -> bool:
        ready_count = sum(1 for i in self.instances if i.status == "ready")
        utilization = 1 - (ready_count / len(self.instances)) if self.instances else 1
        return utilization > self.scale_up_threshold and len(self.instances) < self.max_size
    
    def should_scale_down(self) -> bool:
        if len(self.instances) <= self.min_size:
            return False
        ready_count = sum(1 for i in self.instances if i.status == "ready")
        utilization = 1 - (ready_count / len(self.instances)) if self.instances else 0
        return utilization < self.scale_down_threshold
    
    async def auto_scale(self, factory):
        if self.should_scale_up():
            instance = await factory.create_agent_instance()
            self.register_instance(instance)
        
        if self.should_scale_down():
            oldest = min(
                [i for i in self.instances if i.status == "ready"],
                key=lambda x: x.last_used or datetime.min
            )
            self.instances.remove(oldest)
            await factory.destroy_agent_instance(oldest.instance_id)

Context Transfer Optimization

Agent scaling increases context transfer frequency between workers. Compressing context snapshots, using incremental state synchronization, and caching common context patterns reduce transfer overhead.

Autoscaling Policies

Event-driven autoscaling responds to metrics beyond CPU and memory. Queue depth, average task duration, and context size trends trigger scaling decisions more accurately than raw resource metrics.