queue|onehello@queueone.io

AI systems that do real work in production

The demo is not the hard part any more. The hard part is an agent that runs on a schedule, costs a predictable amount, can be audited after it does something surprising, and does not repeat an action because a step was retried. Retrieval is already serving a live consumer product of mine; the agent framework runs in beta.

Sold on the auditing, cost control, idempotency and evaluation — the parts that decide whether it survives contact with production.

Where it actually goes wrong

A model call is the easiest component in the system. Everything around it is where production risk lives: what happens when the same job runs twice, how you find out what an agent actually did last Tuesday, what stops a retry loop from spending a month's budget in an afternoon, and how you know a change to a prompt made things better rather than differently wrong.

That is ordinary distributed-systems work wearing a new hat — which is exactly why it suits an integration practice rather than a prompt shop.

What you get

An audit trail over agent runs
What ran, when, with what inputs, and what it changed. Retrievable months later.
Idempotent actions
So a retried step does not send the second email, create the second record, or charge the second time.
Cost control
Budgets and ceilings treated as a system property, not a monthly surprise.
Evaluation you can act on
A way to tell whether a change improved the output, rather than shipping on the strength of a good demo.

Where I have done this

  • RAG-based search and analytics running in production in What's The Deal — a live consumer product, not a demo.
  • A first-party multi-agent framework with scheduled execution, auditing and a chat surface.
  • pgvector semantic search over a live listings corpus in KeystoneOS.
  • A vector recommender in production behind What's The Deal, fed by GCP Pub/Sub event ingestion.
  • Multi-agent orchestration with scheduled execution
  • Audit trails over agent runs
  • pgvector semantic search
  • Vector recommenders
  • Evaluation and cost control

Describe it in a paragraph.

Tell me what the system is, what it is doing wrong, and what has to be true by when. I will tell you whether I am the right person for it — including when I am not.

Start a conversationhello@queueone.io

The other three