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AI coding agents for engineering teams

Claude Code and similar agents set up to ship real work in your codebase, with guardrails, tests and review rather than unreviewed pull requests.

Price
Fixed price for the setup and sessions
Timeline
Scoped on a first call
Engagement
fixed-scope project

A setup and coaching engagement for teams adopting AI coding agents such as Claude Code: repository instructions the agent reads first, task sizing, the tests and CI gates that catch its mistakes, review habits, secrets hygiene and cost controls. This site was built that way: an agent working in small, gated commits against a written brief, every change checked by CI.

  1. 01

    Agent instructions for your repositories, such as CLAUDE.md or AGENTS.md

  2. 02

    CI gates matched to your risks: types, tests, linting, security scans and budgets

  3. 03

    A workflow for tasks, reviews and merges with agents in the loop

  4. 04

    Rules for secrets, permissions and what agents may change on their own

  5. 05

    Working sessions with your team on real tickets

Who this is for

  • Engineering teams trying AI coding agents with mixed results
  • Founders who want a small team to deliver like a larger one
  • Leads worried about code quality, security and cost as agent use grows

Who this is not for

  • Replacing your engineering team
  • Generating a whole product that nobody reviews

What this service is

AI coding agents for engineering teams is a setup and coaching engagement for teams adopting coding agents such as Claude Code: repository instructions the agent reads first, task sizing, the tests and CI gates that catch its mistakes, review habits, rules for secrets and permissions, and cost controls. The aim is real work shipped in your codebase, reviewed and tested, rather than a pile of unreviewed pull requests. It is for engineering teams with mixed results from agents, founders who want a small team to deliver like a larger one, and leads worried about quality, security and cost.

This site was built that way: an agent working in small, gated commits against a written brief, with every change checked by CI before it could merge.

Scope

One team and its main repositories. An audit comes first: how the team works today, the repositories, CI, reviews, and where agents already help or hurt. The setup is then committed to your repositories, and the rest of the engagement is working sessions with your team on real tickets.

What is set up and measured

Instructions live in the repository where agents look for them: a CLAUDE.md for Claude Code, or an AGENTS.md, an open format several coding agents read, describing how to build, test and run the project, the conventions to follow and what not to touch.

Gates come before trust. Type checks, tests, linting, dependency and secret scanning, and performance or bundle budgets run on every change, so an agent's mistake fails CI instead of reaching review. Secret scanning with push protection stops credentials reaching the repository at all. Review rules say what an agent may change on its own, what needs a person, and which paths are off-limits.

Tasks are sized so a change can be reviewed in one sitting. And we measure: what each kind of task costs against the time it saves, so the team keeps what pays and drops what doesn't.

What it is not

It is not a way to replace your engineering team, and it is not generating a whole product nobody reviews. Agents are fast and confident, including when they're wrong, so the setup is about making mistakes cheap to catch.

How the engagement runs

  1. Audit

    How the team works today: repositories, CI, reviews and where agents already help or hurt.

  2. Set up

    Instructions, gates and permissions, committed to your repositories.

  3. Pair on real work

    Your team and the agents on real tickets, with me reviewing alongside.

  4. Measure and adjust

    What each kind of task costs against the time it saves, and what to change.

Technologies I use for this

  • Claude Code
  • GitHub Actions
  • TypeScript
  • Playwright
  • Vitest
  • ESLint
  • Model Context Protocol

How this works in your market

How this works in the United States

For US teams, working sessions sit in your morning, my evening, on real tickets from your backlog. Between sessions, I review the agents' pull requests asynchronously and leave written notes the team can learn from.

How this works in South Korea

For teams in South Korea shipping quickly for global markets, the gates double as a safety net for speed: tests, type checks and scans on every change mean the team can move fast without lowering the bar. Korea is three and a half hours ahead of me, so your afternoon sessions are my morning.

Questions about AI coding agents for engineering teams

Will agents write insecure code?

They can, which is why the setup starts with gates: tests, type checks, dependency and secret scanning, and review rules that decide what an agent may change on its own.

Which tools do you support?

Claude Code is what I use every day. The practices (instructions in the repository, small tasks, CI gates and review) carry over to other coding agents.

How do we keep costs predictable?

The right model for each kind of task, limits per session, and measuring what tasks cost against the time they save.

What goes in CLAUDE.md or AGENTS.md?

How to build, test and run the project, the conventions to follow, where things live, and what the agent must not do. Short, specific and kept up to date like code.

Can agents work on our legacy codebase?

Yes, often well, once there are tests around the area they're changing. Adding those tests is a good first task for an agent, reviewed by a person.

Do agents need access to production?

No. Agents work in branches and CI; deploys go through your normal pipeline and approvals.

Sources

  1. Claude Code docs: CLAUDE.md and memory
  2. AGENTS.md
  3. GitHub Docs: secret scanning

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