Your developers have AI coding agents. Is the software getting better?
We set up AI coding agents to work as part of your engineering team, measure the effect and keep the code maintainable.
Tell us how many engineers you have, which AI tools are in use or on trial and how code gets reviewed and released today. We’ll say whether a review, a workshop or a rollout makes sense.
What we take on
We help software teams move from scattered use of AI coding tools to a way of working the whole team trusts. We measure how your team delivers today, set up shared rules, checks and permissions for every agent you use, and train your engineers on your own codebase. Then we measure again. We build client software this way every day, with agents supervised by senior engineers, so the practice comes from our own delivery work. Implementation inside your products is scoped separately.
Max RevittFounder and Director
What needs to work better?
A sentence or two about the problem is enough to start. We will reply within one business day and work out the next step with you.
Prefer to talk? Book a free call.
What it does for your business
The business side first. The software comes later.
A measured starting point
Before changing anything we record how the team delivers now: time from first commit to release, time spent waiting for review, rework and changes that fail in production. The same measures are taken again after the rollout. Licence counts and accepted suggestions tell you who is using a tool. They do not tell you whether the software is improving.
Agent work counts as done only with proof
Every change an agent makes has to pass your tests, type checks and build against the latest code before it reaches a reviewer. Interface changes come with screenshots. Reviewers spend their time on judgement because the mechanical checks have already run.
Several agents at once, without collisions
Engineers can hand separate tasks to several agents at the same time. Each one works in its own copy of the code, claims the files it is changing and hands finished work back with its evidence. Agents can pass work and notes to each other, and what one learns about your codebase is available to the next.
Access agents can use without seeing
Agents get the permissions a task needs and no more. Credentials are supplied when a tool runs, so keys never appear in prompts, logs or code. Commands that could cause damage need a person to approve them, and every action is recorded.
Spending you can predict
The most capable models plan and review, while lower-cost models handle routine edits, with spending limits on each. You get a monthly view of cost against accepted changes, so the bill follows the useful work.
Engineers who stay in charge of the code
Experienced developers are often the most sceptical, with good reason. We work alongside them on real tickets, agree how much agent work the team can review each week and keep pull requests small. Engineers keep the skills to judge what the agents produce, and the team sets the pace of rollout.
What the work can cover
Agentic engineering review
A fixed-scope look at how your team builds and reviews software today, with baseline measures, the risks in current AI use and a written rollout plan.
Embedded rollout
We work inside your team for an agreed period, setting up the rules, checks, permissions and parallel working on your codebase while your engineers deliver real tickets.
Team workshop
Hands-on sessions on your own repository, covering planning, review gates, running several agents and judging their output.
Ongoing support
The tools and models change every month. We keep your rules, permissions and model choices current and re-measure the results each quarter.
Related client work
Gateway.FM
Five years as a technology partner: R&D leadership, hiring, team growth, technical oversight and delivery of core infrastructure.
Global HRIS
A payroll consultancy now sells its own method as software, so it can take on more work without needing more expert hours.
How the work runs
- 1
Free call
We talk through the team, the codebase, the tools in use and what the business expects from them. If the basics such as tests and CI need work first, we say so.
- 2
Measure and review
We take baseline measures from your repository and delivery tools, watch how work moves from ticket to release and write up the rollout plan with a fixed fee.
- 3
Set up and work alongside the team
We install the shared rules, checks and permissions, then pair with your engineers on real work until agent-assisted delivery is part of a normal week.
- 4
Measure again and hand over
After eight to twelve weeks we repeat the baseline measures, report what changed and hand the setup to the people who will own it. Ongoing support is optional.
Questions
Do AI coding agents make experienced developers faster?
The evidence is mixed. A 2025 randomised trial by METR found experienced open-source developers took 19% longer on tasks with AI tools, while believing they were faster. METR’s 2026 follow-up suggests a modest speed-up, with wide uncertainty. Google’s DORA research describes AI as an amplifier of a team’s existing practices. That is why we measure your team before and after, rather than promising a figure.
How do you know whether it is working?
We agree the measures at the start, usually time from commit to release, time waiting for review, rework and changes that fail after release, plus cost per accepted change. We take them from your own repository and delivery tools, then compare the same measures after the rollout. Usage statistics alone are not treated as success.
How do you stop AI-generated code lowering quality?
Agent changes must pass tests, type checks and the build against the latest code before review. Changes are kept small enough to review properly, and the team agrees how much agent work it can review each week. Mistakes become rules in the shared rulebook. Code quality is one of the measures we report.
Claude Code, Codex, Copilot or Cursor: which should we use?
It depends on the work, your security terms and how your team reviews code. We use Claude Code most days and also run Codex and other models for planning and review. The rules and checks we set up work across tools, so you can change tools later without starting again.
Will our source code be used to train models?
That depends on each supplier’s business terms and the plan you are on. We check the terms for the tools you use, set the relevant account controls and agree what code agents may read. We do not offer a blanket guarantee for a supplier’s product.
How do you stop agents leaking secrets?
Agents use credentials without seeing them. Keys are supplied when a tool runs, kept out of prompts, logs and repositories, and secret scanning runs before code is pushed. Permissions are limited to the task, and risky commands need a person to approve them.
Can several agents work on the same codebase at once?
Yes. Each agent works in its own copy of the code and claims the files it is changing, so two agents do not edit the same thing. Finished work lands only after it passes on the latest version. We run our own delivery this way, with several agents working in parallel under a senior engineer.
What do we need in place first?
Version control and a way to run your tests automatically. Agents are only as reliable as the checks they run against. If test coverage or CI is thin, strengthening it is usually the first piece of work, and it pays off for human developers too.
Is this a way to replace developers?
No. The aim is for your engineers to deliver more good work and spend less time on repetitive changes. Agents need people who understand the system, decide what to build and judge the output. We train your team to do that well.
Do you train on our codebase or on examples?
On your codebase, with real tickets from your backlog. Generic exercises teach the tool. Working on your own code shows how agents cope with your conventions, tests and history.
Do you build software this way yourselves?
Yes. Revitt builds client systems with AI agents supervised by senior engineers, and we maintain our own tooling for running agents, including brw, our open-source browser tool. The practice we bring comes from our own delivery work.
Can you work with our team in Sheffield or remotely?
Both. We are based in Sheffield and work with teams across the UK and further afield. Workshops and the start of a rollout often go better in person; most of the embedded work can be remote.
How is it priced?
Each engagement is scoped and quoted as a fixed fee after a free call. The review, workshop and rollout are priced separately, and ongoing support has its own monthly terms. You agree the scope and fee before work starts.
Start with a free call
Talk to Max about your team, your codebase and the AI tools you use. We’ll suggest where to start and what to measure, then send a written scope and fixed fee.
Max RevittFounder and Director