OpenAI · Technologies
Codex
The coding agent your engineers delegate to.
Codex is OpenAI’s coding agent, running in a desktop app, the terminal, your IDE and the cloud, all on one account. Engineers hand it whole tasks: features, refactors, tests and reviews, and it works in parallel across sandboxed copies of your code. We roll it out with the guardrails and the review culture that make it safe.
CODING TASKS · EXAMPLE SPRINT
TASK
SCOPE
REVIEW
STATUS
Bug-Fix-Batch
Repo wide
PR review
DONE
Test-Coverage
Service layer
PR review
DONE
Docs-Update
Repo
PR review
DONE
Refactor-Auth
Module
In review
RUNNING
direct-push-hotfix
No review
Skipped
RISK
Sample sprint
done · running · risk
In plain terms
Autocomplete suggests. Codex delivers.
The shift is from help with lines to delegation of tasks. Here is what changes.
Without it
- Engineers typing every line, assisted or not
- Routine maintenance eating the best hours of the day
- One task at a time, serially
- AI coding happening ad hoc on personal accounts
With it
- Whole tasks delegated and returned as reviewable pull requests
- Mornings that start with three finished pieces of routine work
- Agents working in parallel on isolated copies of the code
- One governed rollout with rules the team agreed to
What CG TECH can do with Codex
The work, broken into the parts that matter.
One agent across every surface
The desktop app, the open source CLI, the IDE extension and the cloud share one account, one configuration and one set of limits, so the agent follows the engineer rather than the other way around.
one agent, every surface
Whole tasks, done properly
Repo instruction files teach Codex your standards, skills encode your team’s workflows, and automations pick up routine work like triage and follow ups unprompted.
whole tasks done, not lines suggested
Speed with the brakes fitted
Tasks run in sandboxes, permissions are explicit and everything lands as a pull request for human review, which is non negotiable. Codex is also a strong reviewer in its own right, catching bugs humans miss.
speed with the brakes fitted
A rollout with numbers behind it
A usage baseline comes with paid ChatGPT plans and heavy use is metered by credits, so we pilot on real work, measure throughput and cost, and scale on evidence.
a rollout with numbers behind it
How an engagement runs
From autocomplete to safe delegation, step by step.
01
Assess
We map your repos, workflows and where delegation pays fastest.
02
Prepare
Instruction files, permissions and review rules set up first.
03
Pilot
A small team runs Codex on real tickets, measured honestly.
04
Scale
Rollout grows with the evidence, with costs tracked.
Questions we hear a lot
Common questions about Codex
What is OpenAI Codex?
Codex is OpenAI’s coding agent. It runs in a desktop app, the terminal, your development environment and the cloud, all on one account, and engineers hand it whole tasks rather than single lines: features, refactors, tests and reviews. It works in parallel across sandboxed copies of your code.
Does Codex replace developers?
No. It changes what they spend time on. The routine work gets delegated and the judgement, architecture and review stay human. Teams ship more, not fewer people.
How is Codex licensed?
Paid ChatGPT plans include a usage baseline, and heavier agentic use is billed through credits on consumption. We model expected usage during the pilot so the bill is a decision, not a surprise.
Is our code used to train models?
On Business and Enterprise plans, no, business data is excluded from training by default. We confirm the right plan and settings as part of the rollout.
Codex or Claude Code?
They are the two leading coding agents and we use both. Codex is fast, token efficient and strong across surfaces; Claude Code is exceptionally thorough on hard refactors. We recommend per team and workload, honestly.
What does it change for a small development team?
It takes on the work that is well defined but tedious: test coverage, refactors, upgrading a dependency across a codebase. That is where small teams lose most of their week, and it is the least interesting part of the job.
How do we stop it putting bad code into production?
The same way you stop a person doing it. Everything goes through review, tests have to pass, and nothing merges without a human approving it. An agent that can commit unreviewed is a process failure, not a tooling one.
Do our engineers need to change how they work?
Somewhat, and it is worth being honest about that. The skill shifts towards writing a clear task description and reviewing output carefully. Teams that treat it as a faster autocomplete get much less from it than teams that hand over whole jobs.
Ready when you are
Putting a coding agent to work? Let us talk.
A discovery session maps your workflows, your risks and your quick wins. You keep the plan either way.
What to expect
- A consultant replies within 4 business hours
- Session booked to understand your requirements
- We will provide you with a fixed price quote