Who this is for
Engineering teams with a real repository, already using AI coding tools or about to, who want that use to be consistent and reviewable rather than personal habit. It fits a single developer with an agent as well as several developers, or several agents, working in parallel. It is not for teams looking for autonomous engineering without developers: people stay responsible for what is merged and deployed.
What the workflow contains
- Assessment: your repository, language and framework, branching, CI/CD and how releases work today
- Risk review: where an agent could do harm in this repository, from unrelated files to secrets to the deployment path
- Task boundaries: what agents may do alone, what needs a person's approval and what is off limits
- Project instructions and safeguards: shared instruction files plus the permissions, sandbox and hooks that actually enforce the important rules
- Isolation for parallel work, so two tasks or two agents do not edit the same working tree
- Gates: tests, build, lint and type checks that must pass before merge or deploy, wired into what you already run where that fits
- Secrets and environment handling rules, and a defined deployment boundary
- Documentation of the operating procedure, with a named owner
- Team handover and onboarding: how to work with agents, and when not to
What your team receives
The end result is a way of working, not a folder of config files. Files such as CLAUDE.md or AGENTS.md, hooks or MCP configuration may be components of it, but they are not the product.
- Documented project instructions for AI-assisted development in your repository
- An agreed workflow for isolating tasks, including parallel work
- Permissions and tool boundaries that enforce the rules that matter
- Defined human review and approval points
- Checks that must pass before merge or deploy
- Rules for secrets and environments
- A written operating procedure with a named owner
- Onboarding for the developers who will work with the agents
- Recommended next-stage improvements, where they are relevant
Why not just install the tool?
Any developer can install Claude Code or Codex, and your team should keep owning how it is used. Installing a tool is a different job from making it safe and repeatable across a team. Installation alone does not decide what an agent may change, how unrelated work is protected, how tasks are isolated, which tests must pass, how secrets are handled, when a person must approve, how several developers and agents work side by side, or how any of it is written down and repeated.
We do not replace your engineers. We help turn the tool into an operating workflow your team runs itself, and that rollout is the part that usually goes missing.
Which tool?
We support Claude Code and OpenAI Codex as assessed cases, and we recommend on your stack, constraints and how your team already works rather than for the tool's own sake. Both have their own page: Claude Code Setup and OpenAI Codex Setup. If your team already uses other assistants, say so; we assess what fits and say plainly where we cannot responsibly configure something.
We are independent: we do not resell subscriptions and do not claim partner status with any tool vendor. Tool behaviour and file conventions change quickly; details on this page reflect vendor documentation reviewed on 30 September 2026 and are re-confirmed against current docs at assessment.
One shared instruction source (AGENTS.md)
Teams that use more than one tool should not maintain several diverging instruction files. AGENTS.md is the convention OpenAI Codex reads, and Claude Code documents a pattern where its CLAUDE.md imports AGENTS.md, so both work from one source. Whether a given tool version picks a file up by default varies, so we confirm it at assessment. Instruction files are guidance the model reads, not enforcement: enforcement comes from permissions, sandboxing, hooks, CI gates and review, and each of those has limits.
Patterns from our own development workflow
These are operating patterns we use internally across LATYNEX projects — an internal system, not a client project. They show how we think about control, not files we sell, and they are adapted to your repository at assessment, not copied into it.
- Work happens on isolated git worktrees, one per task, and each task records which session owns it, so two AI sessions do not edit the same working tree.
- Our AI tooling is configured to block its file-edit tools in a shared integration checkout, so changes reach it by merge. This covers the assistant's edit tools, not every possible way to change a file.
- Production releases run from one designated checkout through a script that refuses a commit not built on what is currently live, refuses a commit not on the canonical remote, and stops on high-severity dependency-audit findings.
- Before a release the script runs type-check, the automated tests, lint and a production build in order; generated route, CTA and pricing maps must be current or the build fails.
- Automated tests pin key counts, so unintended drift fails the suite. There are several hundred of them.
- Commits and pushes made through our AI tooling are scanned for secrets before they leave the machine.
- Project state is kept in short current-state, next-steps and decisions notes, so a new session does not need a full re-read of the repository.
- Our rule is that production deploys happen only with explicit approval for that specific deploy. It is a written policy backed by technical gates, not something the tooling enforces on its own.
What determines the scope
We start with an assessment of your repository and workflow, then write a scoped implementation plan, and only then give a quote and a timeline. There are no fixed packages: effort depends on the factors below. Mention what you can when you enquire; the quote form's free text is enough.
- Repository type, language and framework, and how large and complex it is
- Monorepo or several repositories
- Team size, and how many developers or agents work in parallel
- Branching and worktree strategy today
- CI/CD maturity and the deployment model, including the number of environments
- Test, build and lint coverage
- AI tools already in use and pain points so far
- Security and access restrictions, and how secrets are handled today
- The quality of existing documentation
- The outcome you want
How this differs from neighbouring pages
Development Infrastructure Setup is the base layer: repositories, CI, environments and releases. Claude Implementation for Business and ChatGPT Implementation for Business roll those tools out to non-developer staff; they are not about coding agents. AI Governance & Usage Policy writes company-wide AI usage rules. Custom AI Agent Development builds agents for business processes; the agent there is not a coding agent. This page is about how developers work with AI inside a repository.
Limits
We do not promise productivity multipliers, and we do not claim AI-written code is safe to ship unreviewed. The workflow does not replace code review or engineering judgement, does not make automation safe in every case, and does not fit every repository or every tool. We do not certify security or compliance for any tool. Guardrails reduce risk and have limits: a permission rule can be sidestepped by another route, and a sandbox covers some tools and not others, which is why we combine them and say where each stops. The setup is designed to survive a change of tool.
Scope is set after we have looked at the repository. Timeline depends on repository complexity and existing CI/CD. Project delivery is in English.
How it works
- 1
Repository and workflow assessment
We look at your repository, branching, build, test and release process and the AI tools already in use — read-only, before anything is changed.
- 2
Risks and useful tasks
Where an agent could do harm in this repository, and which tasks are worth delegating to one. You get this in writing, in plain language.
- 3
Permissions, isolation and approval points
What agents may do alone, what needs a person's approval, how parallel tasks are kept apart and where a human must review.
- 4
Project instructions and tool boundaries
Shared instructions for the repository plus the permissions, sandbox and hooks that enforce the important rules, configured inside your own accounts.
- 5
Test, build, lint and CI gates
The checks that must pass before merge or deploy, connected to what you already run where that fits, with secrets and environment rules.
- 6
Validate on real tasks
We run representative tasks through the workflow with your developers and adjust the rules that did not hold up.
- 7
Document and hand over
A written operating procedure with a named owner and onboarding for the team, so the workflow runs without us. Scope and timeline are agreed in writing before configuration starts.
Is this the right page?
- 1 Assess
- 2 Implement
- 3 Automate
- 4 Build
Choose this page if your engineers already use AI coding tools and you want shared rules, secrets handling and review guardrails.
- Choose Development Infrastructure Setup instead if repositories, CI, staging or releases are the bigger gap.
- Choose AI Team Training & Adoption instead if the need is hands-on sessions for a wider team.
- Choose Claude Code Setup instead if you have decided on Claude Code and want its mechanics configured.
- Choose OpenAI Codex Setup instead if you have decided on OpenAI Codex and want its mechanics configured.
What usually comes next
Not a package — only where it makes sense once this is done.
- Development Infrastructure Setup
guardrails work best on a sound base
- Web Application Development
when you would rather have custom software built for you
Who you would be working with
- Company · Who you would be working with
- LATYNEX Digital is a service line of Latynex Trade OÜ, a company registered in Estonia (EU). Contact: info@latynexdigital.com.
- How we work · Delivery
- Remote, in English, with the person who would run the project. No local office is implied in any market.
- How we work · Commercial terms
- One scope and one price, agreed in writing before work starts. Your accounts, code and domain stay yours; any access we use is granted by you and can be withdrawn.
There are no client case studies on this page, and none are implied. What LATYNEX has built and runs itself is on the portfolio, each system labelled by stage. Published prices are on the pricing page; anything not listed there is scoped and quoted after review.
Questions
Does this replace code review?+
No. Review stays with your developers. The workflow adds gates and approval points around it, and we do not claim generated code is safe to merge unread.
Claude Code or OpenAI Codex?+
It depends on your stack, security constraints and how the team works. Both are supported as assessed cases, and we design the workflow so changing tools later is not painful. See the Claude Code and Codex pages for the mechanics of each.
Can you work with the tools we already use?+
Often, yes. Tell us what you use. We assess what can be configured responsibly and say plainly where we cannot.
What happens to our secrets?+
Rules on what an agent may read or paste are a core part of the setup, alongside environment handling and a defined deployment boundary. Secrets stay in your own stores; we do not ask for them in chat or email.
Do we get documentation?+
Yes: a written operating procedure with a named owner, plus onboarding for the team, so the workflow runs without us.
Can't a developer just install Claude Code or Codex?+
Yes, and your developers should keep owning how it is used. Installation does not set task boundaries, isolation, required checks, secrets rules or approval points. We help turn the tool into a workflow the whole team follows; see "Why not just install the tool?" above.
What does it cost?+
There is no fixed price. Effort depends on the repository, the team, your CI/CD and your security constraints (see "What determines the scope"). We assess the repository and workflow first, then give a scoped plan with a quote and timeline.
How long does it take?+
It is scoped after a review of your repository, and the timeline depends on repository complexity and existing CI/CD. We make no time promises here.