Use AI agents across client projects

The bottleneck is rarely skill

In most agencies, the open question is not how something gets built, but who builds it and when. The backlog is full, the people are committed, and the next job waits for someone to become free.

Anvil Coder takes hold exactly there: execution becomes machine work, the decision stays with you. You define requirements and guardrails, the system decomposes them into a graph of atomic work steps and works through them in parallel.

What you can show the client

The most uncomfortable part of a fixed-price project is not the implementation — it is the evidence. “We work to OWASP” is a statement about intentions. “Every work step came into being under the same invariants (OWASP, SOLID, KISS, DRY, Clean Code), and every one leaves its commit behind” is a statement about facts.

With Anvil Coder, guardrails are not a guideline but a condition on every single prompt. Your own rules — a particular layering, a naming scheme, forbidden libraries — can be added and are injected just the same. If you want one of them additionally validated by machine, you store it as a machine-readable rule; as mere prose it acts in the prompt, but not in the validation.

Several clients, one system

Organisations are separated, not sorted

Every client is its own organisation with its own keys, its own projects and its own billing. In the hosted service, the organisation is part of every record and every request, not an after-the-fact filter.

Costs visible per project

What a project has consumed is stated per project — not as a monthly total someone has to back-calculate from.

Caps before it hurts

Cost limits can be set on three levels: per organisation, per project and per run. If a cap is set, a run that breaches it is halted along the way instead of discovered afterwards — without a set amount none applies.

Try one bounded client task

Start with a bug fix or a small feature in one client repository. Agree on acceptance criteria, project rules and who will review the changes. Track the time spent reviewing and correcting the output alongside model usage.

The measured Laravel run shows a fix and regression test, with sandbox verification left unavailable. Use its artifacts and limits as a model for what to record in your own pilot.