Moving AI agent projects from simple demonstrations to production-scale systems often reveals significant engineering challenges. While building a research agent in a few lines of code is straightforward, concurrently running 50 agents, handling failures, managing shared state, and keeping costs under control is where true engineering begins. This article explores seven crucial orchestration patterns designed to tackle these production hurdles. These patterns are framework-agnostic, applicable across various setups like LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, or custom implementations, emphasizing that sound architecture transcends specific libraries.
Pattern 1: The Supervisor with Backpressure
The classic supervisor pattern, where a main agent delegates tasks to multiple worker agents, tends to break down under heavy load. If one worker (e.g., Worker 3) becomes slow, the supervisor might continue to dispatch tasks to it, leading to an ever-growing queue, increased memory consumption, and eventual system collapse.
Backpressure is a mechanism where, instead of crashing, the system intelligently slows down when a worker or the system as a whole becomes overwhelmed. This prevents cascading failures and maintains system stability.
Below is a production-ready Python code example illustrating the key components of a Backpressure Supervisor:
import asyncio
from dataclasses import dataclass, field
from typing import Any, Callable, Optional
from enum import Enum
import time
import uuid
class WorkerState(Enum):
IDLE = "idle"
BUSY = "busy"
OVERLOADED = "overloaded"
FAILED = "failed"
@dataclass
class WorkerAgent:
agent_id: str
name: str
handler: Callable
max_concurrent: int = 3
current_tasks: int = 0
state: WorkerState = WorkerState.IDLE
total_completed: int = 0
total_errors: int = 0
avg_duration_ms: float = 0
@property
def load_factor(self) -> float:
return self.current_tasks / self.max_concurrent
def can_accept(self) -> bool:
return self.current_tasks < self.max_concurrent and self.state != WorkerState.FAILED
@dataclass
class Task:
task_id: str
task_type: str
payload: dict
priority: int = 0 # Lower = higher priority
created_at: float = field(default_factory=time.time)
timeout_seconds: float = 60.0
@dataclass
class TaskResult:
task_id: str
worker_id: str
success: bool
result: Any = None
error: Optional[str] = None
duration_ms: float = 0
class BackpressureSupervisor:
def __init__(
self,
max_queue_size: int = 100,
backpressure_threshold: float = 0.8,
):
self.workers: dict[str, WorkerAgent] = {}
self.task_queue: asyncio.PriorityQueue = asyncio.PriorityQueue(
maxsize=max_queue_size
)
self.results: dict[str, TaskResult] = {}
self.backpressure_threshold = backpressure_threshold
self._running = False
def register_worker(
self,
name: str,
handler: Callable,
task_types: list[str],
max_concurrent: int = 3,
) -> str:
agent_id = f"worker-{name}-{uuid.uuid4().hex[:6]}"
worker = WorkerAgent(
agent_id=agent_id,
name=name,
handler=handler,
max_concurrent=max_concurrent,
)
self.workers[agent_id] = worker
# Store task type mapping
if not hasattr(self, "_type_map"):
self._type_map: dict[str, list[str]]
This code defines essential components: WorkerState enumerates agent statuses, while WorkerAgent dataclass encapsulates properties like max concurrent tasks, current tasks, and load factor. Task and TaskResult dataclasses represent the tasks and their processing outcomes. The BackpressureSupervisor class is the central orchestrator, managing a registry of worker agents, a prioritized task queue, and results. It incorporates a backpressure_threshold to dynamically adjust task distribution, ensuring robust and stable operation by preventing workers from becoming overloaded.