Distributed Mode

In local mode, ARISE stores skills in SQLite and runs evolution in-process. This works well for a single agent process but doesn’t scale to stateless deployments (Lambda, ECS, AgentCore) where multiple instances share a tool library.

Distributed mode decouples the agent from the evolution process using S3 (shared skill store) and SQS (trajectory queue).

Architecture

Quick Setup

1. Provision AWS resources

One command creates the S3 bucket, SQS queue, and DLQ, then saves the config to .arise.json:

pip install arise-ai[aws]

arise setup-distributed --region us-west-2
# Created S3 bucket: arn:aws:s3:::arise-skills-a1b2c3d4e5f6
# Created SQS DLQ:   arn:aws:sqs:us-west-2:123456789:arise-trajectories-abc-dlq
# Created SQS queue: arn:aws:sqs:us-west-2:123456789:arise-trajectories-abc
# Config saved to .arise.json

Or from Python:

from arise.distributed import setup_distributed

config = setup_distributed(region="us-west-2")
# Returns ARISEConfig with s3_bucket and sqs_queue_url populated

To tear down:

arise setup-distributed --destroy

2. Configure the agent process

Use create_distributed_arise() to build an ARISE instance that reads from S3 and reports to SQS:

from arise import create_distributed_arise, ARISEConfig
from arise.rewards import task_success

config = ARISEConfig(
    s3_bucket="arise-skills-a1b2c3d4e5f6",
    sqs_queue_url="https://sqs.us-west-2.amazonaws.com/123456789/arise-trajectories-abc",
    aws_region="us-west-2",
    skill_cache_ttl_seconds=30,   # how often to refresh skills from S3
)

arise = create_distributed_arise(
    agent_fn=my_agent,
    reward_fn=task_success,
    config=config,
)

# Use exactly like local ARISE
result = arise.run(task)

In distributed mode, arise.run() fetches tools from S3 (with cache) and sends trajectories to SQS. Evolution does not run in-process.

3. Run the worker

The worker polls SQS, buffers trajectories, and runs evolution when triggered. Run it as a separate process (ECS task, EC2 instance, or background thread):

from arise import ARISEConfig
from arise.worker import ARISEWorker

config = ARISEConfig(
    s3_bucket="arise-skills-a1b2c3d4e5f6",
    sqs_queue_url="https://sqs.us-west-2.amazonaws.com/123456789/arise-trajectories-abc",
    aws_region="us-west-2",
    model="gpt-4o-mini",
    failure_threshold=5,
    verbose=True,
)

worker = ARISEWorker(config=config)
worker.run_forever(poll_interval=5)  # long-running loop for ECS/EC2

For Lambda (invoked on SQS trigger):

from arise.worker import ARISEWorker
from arise.stores.sqs import deserialize_trajectory

def lambda_handler(event, context):
    worker = ARISEWorker(config=config)
    trajectories = [
        deserialize_trajectory(record["body"])
        for record in event["Records"]
    ]
    worker.process_trajectories(trajectories)

ARISEWorker Reference

class ARISEWorker:
    def __init__(self, config: ARISEConfig): ...

    def run_forever(self, poll_interval: int = 5) -> None: ...
    def run_once(self) -> int: ...
    def process_trajectories(self, trajectories: list[Trajectory]) -> None: ...

Method	Description
`run_forever(poll_interval=5)`	Long-running loop for ECS/EC2. Polls SQS every `poll_interval` seconds, buffers trajectories, and triggers evolution when thresholds are met.
`run_once()`	Poll SQS once, buffer trajectories, evolve if triggered. Returns count of messages processed. Use for cron-style invocations.
`process_trajectories(trajectories)`	Directly process a list of `Trajectory` objects without SQS polling. Use in Lambda handlers where SQS delivers messages via event trigger.

IAM Permissions

Agent process needs:

{
  "Action": ["s3:GetObject", "s3:ListBucket"],
  "Resource": ["arn:aws:s3:::arise-skills-*"]
},
{
  "Action": ["sqs:SendMessage"],
  "Resource": ["arn:aws:sqs:*:*:arise-trajectories-*"]
}

Worker process additionally needs:

{
  "Action": ["s3:PutObject"],
  "Resource": ["arn:aws:s3:::arise-skills-*"]
},
{
  "Action": ["sqs:ReceiveMessage", "sqs:DeleteMessage"],
  "Resource": ["arn:aws:sqs:*:*:arise-trajectories-*"]
}

AgentCore Deployment

See demo/agentcore/ for a complete example deploying ARISE with AWS AgentCore and the A2A protocol.