The Featured Authorized Partner for RabbitMQ — named by the engineers who build it
AceMQ is the Featured Authorized Partner for RabbitMQ, named by the RabbitMQ Core Engineering Team — the people who write and maintain the broker. That recognition covers RabbitMQ support, licensing and professional services, and it makes AceMQ the only RabbitMQ partner with a direct line to the core team. When an escalation needs an answer that is not in the documentation, it does not stop at a support tier.
You do not have to take our word for it — RabbitMQ lists AceMQ on its own site.
See AceMQ listed on rabbitmq.comYou probably need this if…
Where you are now, and where you end up
Concrete state changes, not deliverable counts. This is what actually differs about your RabbitMQ estate when the engagement closes.
Long model calls outlast the delivery acknowledgement timeout, so workers get cut off and the same task runs again.
Timeouts sized per queue to measured task durations, with long chains split into steps that each acknowledge on their own.
When the model API throttles, workers keep pulling tasks they cannot finish and the backlog grows without limit.
Bounded queues, prefetch matched to the provider's rate limit, and back pressure that reaches producers before a memory alarm does.
A poison prompt loops through redelivery, and every pass repeats a tool call that has side effects.
Delivery limits and dead-lettering on every task queue, with idempotency keys on any tool call that changes something outside the system.
Context windows and embeddings travel as message bodies, inflating disk use and slowing every queue they pass through.
Large payloads held in object storage and passed by reference, with message sizes the broker is built to carry.
The agent platform has an owner. The broker underneath it does not, and nobody holds the pager for it.
24/7 cover from a named senior RabbitMQ engineer who already knows the topology, under a contracted response time.
What's covered
How it actually runs
Every phase has a defined duration and a concrete artifact handed over at the end of it. You always know what stage you're in and what you've received.
Workload Intake
We collect the facts that decide the design: how long tasks take at the median and the tail, payload sizes, the rate limits and retry behavior of each model provider, which tool calls have side effects, and what the cluster looks like today. Screen-share and read-only access are the default.
- Task duration and payload size profile
- Model provider rate limits and retry behavior
- List of side-effecting tool calls
- Current cluster inventory and version position
Architecture Review
A senior engineer reads queue types, policies, timeouts, prefetch, dead-letter routing and client behavior against that profile, and checks the cluster against the failure patterns AI workloads hit most. Findings come back ranked by risk, each with the evidence behind it.
- Findings ranked by risk, with evidence
- Queue and policy design for agent task queues
- Timeout, delivery limit and retry settings per queue
- Payload sizing and claim-check recommendations
Remediation and Workload Testing
We apply the changes with your team in your change windows and test them against your actual workload: message sizes, fan-out, consumer behavior and failure injection, including a throttled model API and a worker killed mid-task. Sizing is measured rather than guessed.
- Policy and configuration changes applied
- Workload test results, including failure injection
- Monitoring and alert thresholds for the agent queues
- Runbook for the failure modes specific to your platform
Production Cover
The cluster moves under a support contract or into managed operations, whichever fits your team. Either way the engineer on call already knows the topology, and each quarter the design is reviewed against how the workload has changed.
- 24/7 incident response with a 15-minute emergency SLA
- Named senior engineer on the account
- Quarterly capacity and architecture review
- Direct escalation to the RabbitMQ core team when needed
RabbitMQ problems AI workloads run into, and what we check first
These failure patterns are generic to LLM and agent workloads on RabbitMQ. Each one comes with the first thing an engineer checks and the usual fix.
- Consumers cancelled or channels closed during slow LLM calls — The delivery acknowledgement timeout fired, 30 minutes by default. On older releases the channel closes with PRECONDITION_FAILED; on RabbitMQ 4.3 quorum queues the consumer is cancelled instead, where the client supports it. Either way the message is requeued and the task runs again. We check worst-case task duration against the timeout and set a per-queue value with the consumer-timeout policy key rather than disabling it node-wide.
- Queue depth climbing while the model API rate-limits — Publish rate exceeds what workers can finish, and workers often retry in a loop while holding prefetched messages. We check ack rate against publish rate and the prefetch setting, then bound the queue with a length limit and reject-publish so producers with publisher confirms enabled get a basic.nack instead of an ever-growing backlog.
- Redelivery storms that repeat tool calls — A failing message is requeued again and again, and each pass repeats its side effects. We check the delivery limit, which defaults to 20 on quorum queues since RabbitMQ 4.0, the dead-letter configuration, and whether side-effecting tool calls carry an idempotency key. RabbitMQ delivers at least once, so the consumer has to tolerate duplicates.
- Large payloads rejected or slowing every queue — Documents, context windows and embeddings sent as message bodies. RabbitMQ rejects messages above max_message_size, 16 MiB by default, and large messages grow quorum queue disk use well before that. We move the payload to object storage and pass a reference, the claim-check pattern, which the RabbitMQ documentation also suggests for large messages.
- Memory or disk alarms blocking publishers — A backlog or a pile of unacknowledged deliveries pushes a node past its memory or disk limit, and every publishing connection on it blocks. We check which queues hold the backlog, disk headroom for quorum queues, and whether producers handle connection.blocked notifications instead of timing out silently.
- Tasks that disappear after too many failures — A quorum queue that reaches its delivery limit without a dead-letter target drops the message. We check that every task queue dead-letters somewhere that can be inspected, so a prompt that keeps failing can be found and replayed instead of lost.
Tell us what the broker is doing under your agent workload. A senior engineer reads the ticket first, and on a support contract an emergency gets a response within 15 minutes.
When an AI platform outgrows its first queue
Many agent platforms start on whatever queue was nearest: a Redis list behind Celery, an in-process queue, or a cloud queue that was fine for a prototype. They outgrow it when tasks run for minutes, retries need back-off and a dead-letter route, several teams share one broker, or the data has to stay inside your own network.
AceMQ plans and runs that move onto RabbitMQ as a migration engagement: mapping the delivery semantics that change, running old and new paths in parallel, and cutting over when the evidence says it is safe. Where a different broker is the better fit, we say so.
Real RabbitMQ Results
See how enterprises trust AceMQ for their most critical RabbitMQ workloads.
Questions about RabbitMQ agent workloads
Talk to a RabbitMQ Expert
Send us a message and we'll follow up within one business day — or book a free 30-min consultation directly.
305-204-2607info@acemq.comMiami, FL 33130
Prefer to talk now? Call us directly or use the consultation tab to find a time that works.