AI coding agents for your engineering team, orchestrated and reviewed.

We install the coding orchestrator we use on our own builds into your repository: agents that pick up development and QA work, run inside your CI, and hand every change to a human for review.

01why now

Coding agents such as Claude Code can now read a codebase, plan a change, edit many files, run the tests, and fix what fails, all from a single instruction. Individual engineers who use them well ship noticeably more. Most teams, though, are stuck at the individual level: a few enthusiasts with their own setups, no shared conventions, no idea what the agents are allowed to touch, and no measurement of whether any of it helps.

The step change comes from treating agents as part of the team's process rather than a personal tool: a queue of well specified work, agents that run in a controlled environment, automated checks, and human review at the merge. That is orchestration, and it is how we build our own client software. The same AI-augmented engineering is how Pinned Golf's engineering need went from five engineers to one.

02what we set up

• Agent-ready repositories. Project instructions, architecture notes, and commands written so an agent understands your conventions, plus test coverage on the paths agents will touch.
• Claude Code for the whole team. Shared settings, permission rules for which commands and directories agents may use, custom commands for your common tasks, and connections to your issue tracker and docs through MCP.
• The coding orchestrator. Our orchestration layer takes well specified tickets, runs agents on them in isolated branches, has them write and run tests, and opens a pull request with a summary for an engineer to review. Work that fails checks goes back to the agent or to a person, never straight to main.
• QA agents. Agents that write regression tests for bug reports, extend coverage on untested modules, run end to end checks on pull requests, and file reproducible bug reports when something breaks.
• Measurement. Cycle time, pull request acceptance rate, review load, and escaped defects, tracked before and after so you can see whether it is working.

what agents should and should not do

Good agent work: well specified tickets, test writing, refactors with strong test coverage, dependency upgrades, migrations, documentation, and first drafts of features with clear acceptance criteria. Poor agent work: ambiguous product decisions, security-critical code without expert review, and architecture changes nobody has thought through. The orchestrator routes accordingly, and humans keep the judgment.

03who this is for

Engineering leaders at companies with an in-house team of roughly three to fifty engineers who want more output without matching headcount, and who care about keeping quality where it is. It fits CTOs facing a backlog that never shrinks, QA teams that cannot keep up with release cadence, and companies that have bought Claude or another coding tool and are not seeing the return.

It is not a replacement for engineering judgment, and it will not rescue a codebase with no tests and no review process. We will tell you if that work needs to come first. For broader Claude adoption outside engineering, see Claude consulting and AI training for teams.

04how it runs

1. Assess. We review your repositories, CI, test coverage, review process, and backlog, and pick the workstreams where agents can carry real load first.
2. Fix the budget. A fixed number for setup, rollout, and a measured pilot. Change requests are priced before they start.
3. Pilot on one team. Orchestrator and conventions installed on one repository, with your engineers reviewing agent pull requests from the first week. Results reviewed every two weeks.
4. Train and expand. Hands-on sessions with your engineers on writing tickets agents can execute, reviewing agent code, and extending the setup. Then the next repository.
5. Operate. We stay to tune permissions, add QA coverage, and keep the setup current as the tools change.

05common questions about AI coding agents for teams

What is AI coding orchestration?

Running AI coding agents as part of an engineering team's process instead of as personal tools: a queue of well specified work, agents that run in isolated branches with tests, automated checks, and a human engineer reviewing every change before it merges.

What does the coding orchestrator automate?

Development and QA work that is well specified: drafting features with clear acceptance criteria, writing and running tests, refactors, dependency upgrades, regression tests for bug reports, and pull request summaries. Ambiguous product decisions and security-critical changes stay with people.

Do you use Claude Code?

Yes. Claude Code is the main coding agent in our setups, configured with shared project instructions, permission rules, custom commands, and MCP connections to your issue tracker and documentation. The orchestration approach also works with other coding agents where a team prefers them.

Is it safe to let AI agents change our codebase?

Agents work in isolated branches with scoped permissions, every change has to pass your tests and CI, and an engineer reviews and merges every pull request. Nothing an agent writes reaches main without a human approving it.

Will this replace our engineers?

No. It changes what they spend time on: more specifying, reviewing, and designing, less typing boilerplate and writing tests by hand. The goal is more output and better coverage from the team you have.

How do we know if it is working?

We measure cycle time, pull request acceptance rate, review load, and escaped defects before the rollout and every two weeks after it, so the decision to expand is based on your own numbers.

How much does it cost?

A fixed number agreed before work starts, covering assessment, setup, the pilot, and training. Change requests are priced before they are started, never after. Model and tool usage costs are billed by the providers and estimated up front.