the output, quarter over quarter, measured in completed work with no new bugs.
Engagement · Agentic Engineering
For product, engineering, and data teams.
From writing code to running a factory.
We rebuild how your team ships around AI agents, and train them to run it. Your engineers stop writing code by hand and start directing it, so you ship far more without growing headcount.
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30 minutes.
01 · Proof
Results you might expect.
Real numbers from a team that made this shift.
faster delivery, with nearly all the saving in the build step, from idea to working code.
code written by hand since January. Engineers shifted from authors to managers of agents.
In their words
"We got twice as much done, and no one said they felt burnt out. No one's really coded since January. It's all just managing agents now." CTO of the team profiled
02 · The problem
You bought the AI tools. Your delivery math didn't change.
Every engineering leader is under the same pressure: ship more, ship faster, without adding headcount. So you rolled out the coding agents. A few of your engineers got dramatically faster. But look at what the team actually ships in a sprint, and the number has barely moved.
The reason is that AI landed as an individual tool, not a team capability. Adoption is a barbell. Your strongest people have gone deep and your juniors love it, while the engineers in the middle treat it as a better search box. Everyone works differently, with their own prompts and habits, and nothing they learn is shared. A lesson one person figures out on Monday is invisible to everyone else on Tuesday. A skill someone builds stays on their laptop. Nothing compounds.
This is the wall most teams hit, and it is not a tools problem. The frontier agents are already good enough. The hard part is the operating model: how work gets specified, how agents get run, how their output gets verified, and how every gain gets captured so the whole team has it. That is what we build.
03 · What we build
We install the factory, and train your team to run it.
Two halves that work together. A system built into your codebase that turns scattered AI use into one shared way of working, and the coaching that gets every engineer running it, including the skeptics.
Half one
The system, built into your repo.
Every useful thing your team learns about working with agents goes into one place, in your codebase, where everyone draws from it and adds to it. Concretely, that means:
- A codebase agents can read Context files nested through your repo, so an agent working deep in the code understands how your systems actually work instead of guessing. The single biggest lever on reliability.
- Shared skills, not private tricks The reusable workflows your team builds live in the repo and get reviewed like code. Solve a problem once, and the whole team has it the next day.
- One way to spec work A guided ticket skill everyone uses, from lead to junior, so agents get briefs they can actually build from. Vague tickets are the root cause of bad output.
- Guardrails enforced in code Rules an agent physically cannot bypass, like blocking a push past failing tests. You trust the model with the work, never with the guardrail.
- Work that runs overnight Well-scoped, repetitive work like production bugs and migrations handled in the background, with reviewed PRs waiting in the morning.
Everything in the system changes through the same review as your code, so a senior engineer signs off on every change to the team's tooling. The gains belong to the whole team, not one laptop.
Half two
Everyone trained to use it, and to improve it.
A shared system only works if people actually use it, so the training is built around your real codebase and the framework we set up, not generic AI lessons.
We bring everyone up to a common level first, so the team shares a language and the same baseline habits. Then we push past basic usage to the real unlock: getting people to contribute back. When someone builds a new skill or captures new context, the whole team gets better. That is the difference between a team that consumes AI and a team that compounds with it.
We pay special attention to the people who are stuck or skeptical, mapping each one to a problem they personally care about. The fastest doubter to convert is usually the one who sees an agent solve their own hardest problem in an afternoon.
04 · The end result
What the work looks like once the system runs.
Once the system is running, the shape of the work changes.
Engineers direct, not type
Your engineers stop writing code by hand. They direct agents instead, often several at once, and spend their time on the parts that need a human: deciding what to build, specifying it well, and confirming it works.
Mornings start with finished work
The well-scoped, repetitive work runs in the background overnight, so the team comes in to reviewed PRs waiting rather than a fresh backlog. The exact set of work that runs this way is different for every team, and we design it around yours.
AI is how the team works
The result is a team that ships dramatically faster without growing headcount, where AI is no longer a few people's side experiment but the way the whole team operates.
05 · How the engagement works
From assessment to a running system.
We meet your team where it is today and move it forward from there. No big switchover, no generic rollout.
Assessment
We interview your engineers and leads and audit how your team works today: where AI is being used, where the friction is, and where the biggest wins are. You get a clear roadmap and a prioritized set of quick wins, mapped to your actual squads and codebase.
Implementation and coaching
We build the shared system in your repo and roll it out with your team. We set up the skills, context, standards, and guardrails, implement alongside your dev leads, and coach everyone up to a common level, including the skeptics.
Hypercare
After the system is live, we stay on through the first stretch of daily use, refining the setup and supporting the team while the new way of working becomes the normal way of working.
06 · Who it is for
Built for teams that want more shipped, not more hires.
For product, engineering, and data teams. It is a fit if:
- 01 You have engineers already using AI, or you are behind and want to catch up fast.
- 02 Usage is scattered and you want a system the whole team runs on.
- 03 You have a leader who can commit the team to a shared way of working.
It works across stacks. Web, mobile, and data pipelines each take a slightly different path, and we adapt the system to yours.
07 · Common questions
The questions teams ask first.
Is this about cutting headcount?
No. The goal is more output from the team you have. Engineers move from writing code to directing agents, and the human work, product thinking and confirming the work is right, becomes more important, not less.
Won't agents make a mess of our codebase?
Quality goes up, not down. We shape the codebase so agents understand it, enforce guardrails in code so they can't cut corners, and require a senior engineer to review every change to the shared system. A human owns every change and can explain how it works.
How do you get the skeptics on board?
Adoption is the hard part, and it is most of what the coaching is for. We map each skeptic to a problem they personally care about and let an agent solve it in front of them. The loudest doubter often becomes the heaviest user once they see one of their own hard problems handled in an afternoon.
How disruptive is this day to day?
It works alongside how your team already operates. There is no big switchover. The team picks up a shared language and a couple of light habits, like a regular retro and a session where people share what they have built, and the new way of working settles in from there.
How do you keep AI costs under control?
Costs run up when agents work with no limits, not because spending is unpredictable. The system ties every agent to a defined task and a budget, uses cheaper models for routine work and the top model only where it counts, and reuses shared context so you are not paying to re-explain the same things every time.
What about security, IP, and our data?
The setup uses enterprise agreements with no data retention and no training on your code, with self-hosted and private options where needed. We raise this early, especially for teams handling sensitive data.
Won't all of this change in six months?
The tools will keep changing. The system we build is designed around that: it improves on its own as the models improve, because it is built on your context and standards, not on any one tool.
Does this work for our stack?
Yes. Web, mobile, and data each have their own path, and the system is shaped to fit yours rather than forced into a generic template.
How fast will we see results?
The assessment takes about two weeks and implementation three to four. Meaningful gains show up over the first few months of daily use, as the team builds up shared skills and habits. The case study team reached 2x output over roughly three months.
How do we know it is working?
We track three things: output (work completed without new bugs), speed (idea to production), and adoption (how much of the team is active and contributing). The assessment also gives you a quick-wins list that tends to pay for the engagement on its own.
08 · Talk to us
Ready to make AI how your team works?
Start with an assessment of where your team is today and the fastest path to a system the whole team runs on. 30 minutes is enough to know whether it fits.
Book a call
Or write to us at [email protected]