Short answer: - Pending in Prefect 2.x does not necessarily mean “no agent/worker picked it up.” Typically: - Scheduled: the run is queued to start (if it’s past its start time you’ll see it as “Late” in the UI but the state is still Scheduled). If no agent/worker is polling, runs will remain Scheduled/Late. - Pending: usually indicates a worker has claimed the run and is preparing infrastructure; it’s a brief transitional state before Running. - Running: the code is executing. So if you’re seeing lots of Scheduled/Late runs, that’s a sign nothing is picking them up. If you’re seeing Pending, it often means a worker has claimed the run and it’s about to start. Autoscaling ECS on queue depth: - Yes, you can autoscale an ECS worker service based on “queue depth.” We generally recommend scaling on runs that are due to start (Scheduled with expected start time <= now). Optionally you can include Pending if you want to scale while workers are preparing infra. How to get the queue depth programmatically (Prefect 2.x): - Use the Prefect client’s count endpoint with filters for your work queue name and state types. Example: count due Scheduled runs and Pending runs for a given work queue, then publish as a CloudWatch custom metric for ECS Application Auto Scaling:

from datetime import datetime, timezone

from prefect.client.orchestration import SyncPrefectClient
from prefect.client.schemas.filters import (
    FlowRunFilter,
    FlowRunFilterStateType,
    FlowRunFilterWorkQueueName,
    FlowRunFilterExpectedStartTime,
)
from prefect.client.schemas.objects import StateType

import boto3

WORK_QUEUE = "my-queue"
REGION = "us-east-1"

now = datetime.now(timezone.utc)

with SyncPrefectClient() as client:
    scheduled_due = client.count_flow_runs(
        flow_runs=FlowRunFilter(
            state_type=FlowRunFilterStateType(any_=[StateType.SCHEDULED]),
            work_queue_name=FlowRunFilterWorkQueueName(any_=[WORK_QUEUE]),
            expected_start_time=FlowRunFilterExpectedStartTime(before_=now),
        )
    )

    pending = client.count_flow_runs(
        flow_runs=FlowRunFilter(
            state_type=FlowRunFilterStateType(any_=[StateType.PENDING]),
            work_queue_name=FlowRunFilterWorkQueueName(any_=[WORK_QUEUE]),
        )
    )

backlog = scheduled_due + pending  # or just scheduled_due, depending on your policy

cw = boto3.client("cloudwatch", region_name=REGION)
cw.put_metric_data(
    Namespace="Prefect",
    MetricData=[
        {
            "MetricName": "QueueBacklog",
            "Dimensions": [{"Name": "WorkQueue", "Value": WORK_QUEUE}],
            "Timestamp": datetime.utcnow(),
            "Value": backlog,
            "Unit": "Count",
        }
    ],
)

Then configure an ECS Application Auto Scaling policy to target this CloudWatch metric for your worker service. References: - Prefect 2 states: States in Prefect 2 - Work pools/queues and workers: Work pools and workers - Agents overview (historical in 2.x): Agents If you can share: - Whether you’re using agents or workers - The exact work pool/queue(s) you want to scale on - ECS Service setup (Fargate vs EC2) …I can tailor the filter logic and the CloudWatch policy details to your setup.