7 AI Agent Orchestration Patterns for Scaling Concurrent Systems (With Production Code)

Every AI agent framework has a "build a research agent in 10 lines" tutorial. Cool. Now try running 50 agents concurrently, handling failures, managing shared state, and keeping costs under control. That's where demos die and engineering begins. These are 7 orchestration patterns that work across frameworks — LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, or your own custom setup. The patterns are framework-agnostic because good architecture outlasts any library. Pattern 1: The Supervisor with Backpressure The classic supervisor pattern — one agent delegates to workers — breaks down under load. Worker 3 is slow, but the supervisor keeps sending it tasks. Queue grows. Memory grows. Everything dies. Backpressure means: when a worker is overwhelmed, the system slows down instead of crashing. 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" OV