AI Coding Agents Development

Those are excellent assistants for a developer typing in an editor. A coding agent works asynchronously on a defined job — reviewing a PR, sweeping a migration across two hundred files, reproducing a bug — without a human driving each step. Most of our clients run both: the assistant helps individuals write code, the agent handles the repository-wide work that no individual wants to own.

The agent does, and we scope that access tightly: read access to the repos in question, write access limited to branches, no production credentials, and no secrets in the model context. Where policy requires it we deploy entirely inside your infrastructure with self-hosted models, so no code crosses your boundary at any point. We work under NDA from the first conversation.

That is usually where it earns the most, because legacy work is exactly the work engineers avoid. The constraint is not size but coherence: if conventions are inconsistent, the agent needs stronger grounding — and the indexing phase surfaces those inconsistencies, which many teams find valuable on its own. We have found large monoliths are a better fit than they sound.

It never merges. Every change lands as a PR under your normal review and CI, and the agent must produce a green build before it opens one — generated tests are executed, not just written. Beyond that we track merge rate per category and pull back autonomy from any category where quality drops, so a regression narrows the agent scope automatically.

Anything with a mature toolchain — TypeScript and JavaScript, Python, Go, Java and Kotlin, C#, Ruby, PHP, Rust, Swift. Practically, the quality bar depends less on the language than on the state of your test suite and CI: a codebase where tests are fast and meaningful gets far more out of a coding agent than one where CI is red half the time.

Inference cost scales with volume of PRs and repository size, and we optimise it deliberately — cheap models for classification and routing, frontier models only for the genuinely hard reasoning. We instrument cost per merged PR from day one so the value is measurable against a number rather than argued about in the abstract.