AI coding agents · Built in Switzerland
AI coding agents carry out software tasks inside a repository. Anvil Coder coordinates their work around requirements, project rules and configurable checks. You define the task and review the changes; the hosted service hands over a draft pull request.
Describe the change, acceptance criteria and boundaries. Your repository and project rules provide the working context.
Anvil plans small work units and their dependencies. Agents implement those units and leave traceable commits.
Review the changes, executed checks and unresolved findings together. A person decides whether to merge in the hosted workflow.
Illustrative task · not a benchmark
A specific failure case is a useful way to evaluate AI software development on your own project. Describe observable behaviour in the task:
During review, inspect the diff, test changes and evidence that the tests ran. A generated test file alone does not establish that the bug is fixed.
Autonomous coding agents need a clear assignment and appropriate approval points. Anvil lets you configure oversight per project. Without a configured approval level, a started task runs through; a person retains the merge decision in the hosted service.
Guardrails carry your architecture and quality rules into the work. Executable rules can report or block violations. An instruction in a prompt alone does not establish compliance.
An existing repository, clear requirements and repeatable check commands are a useful starting point. First establish that the execution environment has the tools your project needs. Java and Spring Boot are the most established path; other stacks depend on their toolchain.
Anvil is developed by Halvic Labs in Switzerland. Your model provider’s data processing and credentials are also part of choosing how the project runs.