AI Coding Tools
Codex
OpenAI’s Codex, put to work properly — an agent that reads a repo, writes code and opens pull requests, run by engineers who keep humans in control.
Overview
The name Codex has a history worth being straight about. The original Codex was OpenAI’s 2021 code model, the engine behind the first version of GitHub Copilot, and it was retired years ago. Today “Codex” means something different: OpenAI’s current agentic coding system, built on its coding-tuned models. It comes in two forms that share the same idea — the Codex CLI, an open-source, terminal-based agent that runs on your machine, and cloud Codex, which runs the same kind of agent in OpenAI’s environment. Both can read a repository, plan a change, write and edit code across many files, run commands and tests in a sandbox, and open a pull request for review.
That makes Codex OpenAI’s answer to agentic development — the same category as Anthropic’s Claude Code, and a step beyond the autocomplete-in-the-editor model that Copilot and Cursor grew up on. The distinction matters. An autocomplete tool suggests the next few lines while you type; an agent takes a task, works through it across a whole codebase, and comes back with a diff and a passing test run — or an explanation of why it could not. We use Codex first-hand on real work, so what we tell you comes from operating it, including the days it saves and the times it is confidently, expensively wrong.
Our position is that an agentic coding tool is a force multiplier for good engineers and a liability for a team that treats its output as finished work. Codex does not remove the need for engineering judgement, tests or code review — it moves that judgement to the front of the process and raises the volume of change a small team can review. We use it that way ourselves, and when we help clients adopt agentic AI development, the harder half of the job is never the tool. It is the review discipline, the guardrails and the sandboxing that let a team move faster without shipping work nobody understood.
Best for — Well-scoped, verifiable engineering work in the hands of a senior team that reviews every diff — and teams who want to adopt agentic AI development with the guardrails it actually requires.
Why teams choose Codex
Whole-task work, not line-by-line hints
Codex takes a described task and works it end to end — reading the relevant files, making the change across the codebase, running the tests, and returning a diff. That is a different unit of work from autocomplete, and it is where the real time saving lives: on the mechanical, verifiable changes that used to eat an afternoon.
The sandbox does the tedious verification
Because Codex runs commands and tests in a sandbox, it does not just propose a change — it tries it and tells you whether the tests pass. That closes the fastest feedback loop in engineering, and it is what separates an agent that produces working diffs from one that produces confident guesses.
Higher review-led throughput for a small team
Used with discipline, Codex shifts a senior engineer’s day from typing to reviewing, and lets them keep several changes moving at once. The output of the team goes up without the headcount — as long as the review capacity is real, which is the constraint we design around rather than wish away.
Why businesses choose Codex
- You want an agent that completes verifiable engineering tasks end to end, not another autocomplete plugin, and you have work that is well-scoped enough for that to pay off.
- You are already invested in OpenAI’s models and ecosystem and want a coding agent that fits that stack cleanly.
- You want the open-source Codex CLI in your own terminal, where you can watch what the agent does and keep it inside boundaries you set, rather than a fully opaque service.
- You want a team that uses Codex on real work and will set up the sandboxing and review discipline honestly — including telling you where an agent is the wrong tool for the job.
What we build with Codex
The capabilities this technology is genuinely strong at — and what we most often build with it.
The Codex CLI in your terminal
The open-source Codex CLI runs the agent locally, inside the project you point it at. We use it where teams live — in the terminal, next to the repo and the tests — so the agent works with your real toolchain and you can see every command it runs rather than trusting output from elsewhere.
Cloud Codex for parallel, offloaded tasks
Cloud Codex runs the same kind of agent in OpenAI’s environment, which suits handing off several independent tasks at once and collecting the resulting diffs. We use it for parallelisable, well-bounded work while keeping anything sensitive on the local CLI where the boundary is ours.
Repository-aware, multi-file editing
Codex reads across a codebase to understand context before it changes anything, then edits multiple files coherently to make one change land properly. That whole-repo awareness is what lets it handle migrations and refactors rather than isolated snippets.
Sandboxed command and test execution
The agent runs shell commands and test suites in a sandbox as part of doing the task, so a change is exercised before it reaches you. We treat the sandbox as a control surface — deciding what it may run and touch — not merely a convenience.
Pull-request-shaped output
Codex packages its work as a reviewable change — a diff, and on the cloud side an actual pull request — which keeps the human review step exactly where it belongs. We wire this into normal code review so an agent’s work goes through the same gate as anyone else’s.
Configurable autonomy and approvals
Codex can run with different levels of autonomy — asking before it acts, or working more freely within set bounds. We tune that per task and per repository, giving the agent more rope on low-risk, well-tested work and keeping it on a short leash where a mistake would be expensive.
Use cases
Large refactors and migrations
Mechanical but sprawling changes — moving to a new API, renaming across a codebase, upgrading a framework — where the change is repetitive and the tests tell you it worked. This is Codex’s strongest ground: high effort, clearly verifiable.
Test coverage and bug fixing
Writing tests for under-covered code, or fixing a bug with a clear reproduction, where the agent can make the change and prove it against a failing-then-passing test. The verification loop does the work that used to be tedious.
Backlog clearance for a small team
Working through a queue of well-defined, low-ambiguity tickets in parallel, so a senior engineer spends their day reviewing diffs rather than typing them — turning review capacity, not headcount, into throughput.
Helping a team adopt agentic development
Setting up Codex properly inside an existing engineering org — sandboxing, approval levels, review workflow, guardrails on what the agent may touch — so a team moves to agentic development deliberately instead of letting an autonomous tool loose and discovering the failure modes in production.
When Codex is the right choice
- You have a well-scoped, verifiable engineering task — a migration, a refactor across many files, adding tests, wiring up an integration, fixing a bug with a clear reproduction — where an agent can make the change and a passing test run tells you whether it worked. Codex is strongest exactly where success is checkable.
- You want to raise the throughput of a senior team, not replace it. Codex lets one engineer drive several changes in parallel and spend their time reviewing diffs rather than typing boilerplate — provided the reviewing actually happens.
- You are a team trying to adopt agentic AI development deliberately, and you want the sandboxing, review workflow and guardrails set up properly rather than letting an autonomous agent loose on a codebase and hoping.
- Wrong for: sprawling, ambiguous work with no clear definition of done — an open-ended architecture decision, a subtle product judgement, anything where nobody can say what “correct” looks like. An agent given a vague task will produce plausible code that misses the actual point, and reviewing it costs more than doing it.
- Wrong for: any team that will not review the output. Codex can be fluently, confidently wrong, and an agent’s mistakes look exactly like its good work. If a diff is going to be merged unread, an agentic tool is a way to accumulate problems faster, not a productivity gain.
Codex: pros and cons
Strengths
- A genuine agent: it reads the repo, edits across many files, runs commands and tests in a sandbox, and opens a pull request — a whole task, not a suggestion in the margin.
- The open-source Codex CLI runs in your terminal on your machine, so it fits into existing workflows and you can see exactly what it is doing rather than trusting a black box.
- Backed by OpenAI’s coding-tuned models and its wider ecosystem — the same API, tooling and model line many teams already build on.
- Verification is built into the loop: running the tests is part of how it works, so you get diffs that have actually been exercised rather than untested generated code.
Trade-offs
- Agentic tools can be confidently wrong. Codex will produce clean, plausible code that solves the wrong problem or introduces a subtle bug, and it states it with the same confidence as its good work. Nothing it produces is safe to merge without a senior reading it.
- Sandboxed autonomy has real limits. The sandbox is what makes it safe to let an agent run commands, but it also means restricted network, environment and permission boundaries — and an agent that cannot reach something it needs will work around it or quietly do the wrong thing.
- Heavy use costs real money. An agent that reads large parts of a codebase and iterates through test runs consumes a lot of tokens and compute, and at team scale that is an operating cost you feel monthly, not a rounding error.
- It ties you to OpenAI’s models and ecosystem. Codex is OpenAI’s agent, tuned to OpenAI’s models; adopting it deeply is a bet on that provider’s roadmap, pricing and availability, and it does not remove the underlying need for engineering judgement, tests and code review — it relocates it.
Architecture
The heart of Codex is its execution model, and understanding it is most of understanding the tool. The agent operates in a loop — read the relevant code, plan a step, edit files, run commands and tests in a sandbox, observe the result, and repeat — until the task is done or it gives up and explains why. The sandbox is not incidental. It is the boundary that makes it acceptable to let an agent run arbitrary commands, and it is where we spend design effort: deciding what filesystem, network and permission access the agent has, so it can do the job without being able to reach past it.
Around that loop we put the same structure we would give any component that acts autonomously. The Codex CLI runs locally, which keeps code and execution inside your own machine and lets us watch every command; cloud Codex trades that for scale and parallelism, and we place work on whichever side matches its sensitivity. Autonomy is configured per task — how much the agent may do before it asks — so the level of trust is a deliberate setting, not a default we inherited.
Crucially, the agent’s output rejoins the normal engineering process as a reviewable diff or pull request. We do not build workflows where agent-generated code merges itself. It goes through the same review gate, the same CI, and the same tests as human work, because the agent is a fast, tireless junior that must be checked — not a trusted author. That review step is the load-bearing part of the architecture, and any design that skips it is building trouble at speed.
Performance
For an agent, “performance” is really two things: how much wall-clock time it saves you, and how much token and compute cost it burns doing so. Those trade against each other. A task that involves reading a large codebase and iterating through several test runs is genuinely expensive in tokens, and letting an agent grind on an under-specified problem is the fastest way to spend money for a poor result. The work is in scoping tasks tightly enough that the agent converges quickly, and knowing which jobs are worth handing to it at all.
The biggest wins come from choosing well. On a well-defined, verifiable task the agent can compress an afternoon of mechanical editing into minutes and prove the result with a test run — a real gain. On an ambiguous one it produces plausible work that takes longer to review and correct than it would have taken to write. We treat compute spend as a first-class metric, cap how long an agent may run before it checks in, and measure the honest figure: not raw speed, but net engineering time saved after review is counted.
Security
Two facts shape how we secure Codex work: the agent runs commands, and — in its cloud form and via its models — code and context leave your machine. Both are manageable, but only by design. The sandbox is the first line: we constrain the agent’s filesystem, network and permission access so that even if it does something wrong, the blast radius is contained. An agent that can run arbitrary shell is exactly as dangerous as what that shell can reach, so limiting the reach is the whole game. Where code is sensitive, we favour the local CLI over the cloud so the boundary stays ours, and we keep secrets and credentials out of anything the agent can see.
The second line is review discipline, and it is a security control, not just a quality one. Agent-generated code can introduce a vulnerability as fluently as a feature — an injected dependency, a weakened check, an unsafe default — and it will look entirely reasonable. So nothing an agent writes is trusted until a senior has read it, and the agent’s output goes through the same review, CI and dependency checks as any human contribution. Configurable approvals mean the agent asks before it does anything consequential on higher-risk work. Humans stay in control by construction: the agent proposes, tests and explains; a person decides.
Scalability
Scaling agentic development is not a servers problem — Codex’s heavy lifting runs on OpenAI’s side — it is a people-and-cost problem. The genuine constraint as you lean on an agent is review capacity: an agent can produce far more change than a team can carefully read, and the moment output outruns review, you are scaling risk rather than throughput. So we design for review to be the bottleneck we protect, keeping tasks small and diffs legible so a human can actually judge each one, rather than chasing raw volume.
The other ceiling is cost and provider limits. Heavy agentic use consumes real compute, and you are working within OpenAI’s rate limits, pricing and roadmap — a dependency to plan around, not assume away. We build for graceful behaviour under throttling, keep the agent scoped to work where it earns its cost, and are honest that adopting Codex deeply is a bet on one provider’s ecosystem. The way to scale it safely is to grow the agent’s remit only as fast as your ability to review and pay for its output, not faster.
Codex integrations & ecosystem
The technologies we most often pair with it — each links to how we work with it.
How we work
We start by drawing the line between work an agent should touch and work it should not. Well-scoped, verifiable tasks — where a test run can tell you it worked — go to Codex; ambiguous, judgement-heavy work stays with an engineer. Then we set the tool up properly: sandbox boundaries, autonomy and approval levels tuned per repository, and the agent’s output wired into your existing review and CI so nothing it writes merges unread. The setup is the deliverable as much as any code, because a badly configured agent is worse than none.
From there we use it the way we would want a client to: diffs reviewed by seniors, compute budgets watched, and an honest tally of net time saved after review. When the engagement is helping your team adopt agentic development, we work alongside your engineers so the review discipline becomes theirs, not a habit that leaves when we do. Because we operate what we build and use Codex on our own work, the guidance is first-hand — including telling you where an agent is the wrong tool and plain engineering is faster.
The service behind it
Delivered throughAI DevelopmentWhat we build with Codex
The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves — including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Industries we use Codex in
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
Also in AI Coding Tools
Why teams choose us for Codex
We use Codex on real work
This is not a tool we read about. We run agentic coding agents on our own engineering every day, so our advice on where Codex saves real time and where it produces confident nonsense is first-hand and blunt, not repeated from a launch post.
Senior engineers who review everything
An agent is only as safe as the review behind it, and the review only works if the reviewer is good enough to catch what the agent got subtly wrong. The people running Codex on your work are senior, and the review discipline is the point of the engagement, not an afterthought.
Honest about the trade-offs
We will tell you where an agent is the wrong tool, what heavy use costs, and that adopting Codex ties you to OpenAI’s ecosystem. Agentic development is a real gain on the right work and a liability on the wrong work, and we would rather you knew the difference than bought the pitch.
We set it up, not just run it
When you want your own team to adopt agentic development, we build the sandboxing, approvals and review habits into how they work — so the capability stays after we leave rather than walking out the door with us.
Typical timeline
- 01
Discovery and fit
One to two weeks mapping where an agent earns its cost in your codebase, which tasks are verifiable enough to hand over, and what the review and compute picture looks like — so the decision is evidence-based, not hype-based.
- 02
Setup and guardrails
Standing up Codex properly: sandbox boundaries, autonomy and approval levels per repository, and its output wired into your existing review and CI so agent work goes through the same gate as everyone else’s.
- 03
Proof on real work
Two to three weeks driving a genuine, well-scoped task — a migration, test coverage, backlog clearance — end to end, measuring net engineering time saved after review, so the value is proven on your code rather than a demo.
- 04
Adopt and hand over
Rolling the workflow out with review discipline embedded in your team, compute budgets and monitoring in place, and handover so your engineers run agentic development themselves once we step back.
How pricing works
- Discovery and feasibility — a fixed-price assessment of where agentic development fits in your codebase and workflow, which tasks are worth handing to an agent, and what the review and compute costs realistically look like before you commit.
- Fixed-scope engagements for a defined piece of work — a large migration or refactor driven with Codex, or standing up the sandboxing, approvals and review workflow for your team — quoted once the scope is understood.
- Monthly senior engagement for teams adopting agentic development, where we work alongside your engineers to build the review discipline and guardrails into how they work, not just deliver a one-off.
- Note that model and compute usage — the token cost of running Codex — is a separate, ongoing operating expense we design to keep in proportion, distinct from our engineering fee.
Hire Codex engineers
Need Codex capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.
Hire Codex engineersCommon questions
Is Codex the same thing that used to power Copilot?
No, and it is worth being precise. The original Codex was OpenAI’s 2021 code model that powered the first version of GitHub Copilot, and it was retired. Today “Codex” refers to OpenAI’s current agentic coding system — the open-source Codex CLI and cloud Codex — built on newer coding-tuned models. It is a whole different category from that early autocomplete engine: an agent that reads a repo, edits across files, runs tests in a sandbox and opens pull requests, rather than a model that suggests the next line.
How is Codex different from Copilot, Cursor or Claude Code?
Copilot and Cursor grew up as autocomplete and AI-editor tools — they suggest and assist while you type in the IDE. Codex is an agent: you give it a task and it works across the codebase, runs tests, and comes back with a diff. In that sense it sits in the same category as Anthropic’s Claude Code, the other leading terminal-based coding agent. The honest short version is that Codex is OpenAI’s model-led coding agent; Claude Code is Anthropic’s. We use both and pick per task rather than treating any one as the answer to everything.
Can we let Codex merge its own code?
No — and we will not build a workflow that does. Codex can be confidently, fluently wrong, and an agent’s mistakes look exactly like its good work: clean code that solves the wrong problem or hides a subtle bug. Its output rejoins your normal process as a reviewable diff or pull request and goes through the same senior review, CI and tests as any human contribution. The agent proposes, tests and explains; a person decides. Skipping that review is the single fastest way to turn a productivity tool into a liability.
What does it cost to run Codex at team scale?
There are two costs: our engineering fee and the ongoing token and compute cost of running the agent, which is a separate operating expense. An agent that reads large parts of a codebase and iterates through test runs is genuinely expensive, and letting one grind on an under-scoped task burns money for a poor result. We scope tasks tightly, cap how long an agent runs before it checks in, and measure net engineering time saved after review — so you pay for real gains, not for an agent spinning on ambiguous work.
Does adopting Codex lock us into OpenAI?
To a degree, yes, and we would rather say so plainly. Codex is OpenAI’s agent, tuned to OpenAI’s models, so leaning on it heavily is a bet on that provider’s roadmap, pricing and availability. That can be the right bet if you are already invested in that ecosystem. What it does not do is remove the underlying need for engineering judgement, tests and code review — those stay yours whichever agent you use, which is also what keeps you able to change tools. We help you adopt it with eyes open, not as a one-way door.
Building on Codex?
A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.