Your agents already write code. You just cannot trust it yet.
The agent writes code in minutes, and you still read every diff twice, looking for the place where it did something almost right. We teach you to build with agents the way we deploy in production: with steering, verification and a trace.
Instead of a talk about trends we work on your real code: how to make the agent get it right the first time and repeatably, so you stop cleaning up after it after hours. Led by engineers whose agent-built systems run in production under real constraints.
- You talk to an engineer, not a salesperson.
- 30 minutes, no sales presentation.
- No prep or homework on your side.
In short
- Price
- Quoted after a calldepends on format and scope; we give you a real figure after one call about your repo
- Format
- 1:1 or a company course, onlineon your real codebase instead of exercises
- Who teaches
- Engineers building with agents in productionthe program comes from our builds under load
- For whom
- A dev, founder or solo buildernot for non-technical teams (see AI training)
Participant reviews
What they say after 1:1 sessions.
Kuba Koziej CEO & co-founder of MoreGrowth, board member at Natu.Care
I went from barely shipping a frontend to building full applications with a backend and a database, and deploying them safely instead of hoping they would hold. We worked through the parts I kept tripping on: Docker, the coding agents and tools, and when it is worth reaching for skills, MCPs and connectors. It stayed practical the whole way, on what I actually wanted to build.
Hlib Utkin
Public administration
A large stream of documents passes my desk every week. I now review, draft and prepare them with Claude in a fraction of the time, and research that used to eat an afternoon takes minutes. It is wired into the services I already work in, so the documents reach me instead of me hunting for them.
Ivan Chepurin Senior Software Engineer, Immutable
I came in using coding agents ad hoc and left orchestrating them: loops, an agent kanban, a full development cycle that ships. What stayed with me is running context and cost on purpose, and building my own harnesses instead of waiting for a tool to ship one. It changed how I work day to day.
Oleksandr Usyk Co-founder & Art director, jakotako
I run a design studio, not an engineering team, and I still left with something working. We built an agent loop that pulls in leads and keeps an eye on the competition, and I put my own site together with Claude. It was hands-on, on my real work, not a talk about AI.
The agent built it in an hour. How long do you then spend checking after it?
The agent drifts off the spec. Requirements written in prose rot. Tests pass while the product does not work. Documentation dies at the first code change. And the old code nobody fully understands sits untouched.
It is the same method our own builds run on, in production, under load. See case studies →
Which level do you work at today?
The whole developer market today fits into three levels of working with AI. Read the descriptions and find where you stand; one sentence is usually enough to recognize yourself.
~65%
Level 1
AI Curious
Trying it, not shipping with it
Has a ChatGPT account, pastes in code snippets, sometimes gets a useful hint. An agent in the repo? Tried once, got burned once, went back to working by hand.
~30%
Level 2
Usually you
AI Literate
Tools paid for, trust still manual
Pays for the tools, writes good prompts, and the agent genuinely speeds up single tasks. But trust is manual: they still read every diff twice, because the agent gets things almost right. Scale ends at their own eyes.
~5%
Level 3
Where the course takes you
AI Native
Trust lives in the system
Does not read every diff, because they do not have to: the agent works inside set bounds, and the output passes acceptance tests and evals before a human ever sees it. Trust lives in the system, not in the eyes.
You do not get from level two to level three with another tool; you get there with a way of working. That is exactly what this course teaches, and that is where the name comes from: AI-Native.
AI-Native Harness
What changes once you take the wheel.
Three things that turn “the agent kind of worked” into “I can trust it”: control, verification, and a method that stays with you.
Control over hope
The agent works inside the bounds you set. The result is repeatable.
- A human approves where it matters
Verification over trust
Green means it works because we verify the product's behavior from the outside, not with tests the agent wrote for its own code.
- The only-probable answer gets caught
A method that stays with you
You leave with your own written method. It is repeatable on the next project, and the same method can onboard a team once you grow.
- Long projects that do not drift
What you will learn.
Each module pairs working with agents with the discipline that makes the output trustworthy. We fit the scope to your stack and your goal.
Theory · groundwork
How agents really work and where they fail, before we touch the project.
01GroundworkHow agents really work, and where they fail
Where the answer that only looks right comes from, and the most expensive failure mode: an unsupervised agent cements accidental decisions into its own tests, and every later session is afraid to touch them.
- 02Trust
From “the agent kind of worked” to “you can trust it”
Today trust is manual: you read every diff twice. We show how to move it into the system, meaning what the agent has to prove before its output even reaches your review.
Practice · your codebase
You drill on your own code and finish with a pilot.
03ControlAn agent that gets it right the first time
Context discipline: a twenty-line AGENTS.md and a docs tree the agent lists at the start of every session. You document only surprises, the delta between the internet and your project, and stale plans stay out of context so the agent does not go off fixing history.
What you leave with
An AGENTS.md and a docs tree your agents actually load
- 04Verification
Requirements that verify themselves
Executable specifications: requirements written as given-when-then acceptance tests the agent must pass before the result reaches review. You stop checking by eye what a machine can check for you.
What you leave with
A set of executable specs for one real flow in your repo
- 05Discipline
A long project that does not drift
A golden set and an eval loop: a baseline score, an experiments folder, only improvements get committed, and a failed experiment stays written down as knowledge. For tasks with a measurable end state we add agentic loops: the agent runs the cycle itself until the score moves.
What you leave with
A golden set with an eval script for one metric you care about
- 06Pilot · Scale
A pilot, and a method that stays
You close the course with a real pilot done the new way, and you write the method down so it survives us leaving. The same method later onboards your team.
What you leave with
A working pilot, and the method written down so it works without us
Builders and teams, one method.
The depth depends on who is in the room, but you leave with a method that stays in your workflow. A whole team? That is the company course: as many developers as you need, on shared code, with shared conventions and a pilot at the end.
A technical founder
A founder who codes and ships product themselves.
- A repeatable way to build with agents
- An agent that gets it right the first time
- What to automate first, what to keep human
- You leave with a working setup and your own method
A solo builder or small team
An indie hacker or a small team moving into agent work.
- Agent work that holds in production-shaped conditions
- Control and verification you can trust
- Context and cost kept on a budget
- A method that grows with you
Dev team lead
Your team already uses agents, each person differently, and review chokes more every week.
- Shared conventions for working with agents
- Lower review cost
- Onboarding new people with the same method
- A pilot on shared code
Formats and price
1:1 or a company course, on your repo.
The same method, online: one-on-one, or a company course with as many developers as you need, always worked on your production-shaped code. The final quote depends on scope, the stack and how many people take part.
1:1 intensive
A founder, engineer or solo builder who keeps building after the course.
One-on-one on your repo: we stand up the method on your codebase and the way of working that holds after we leave.
Quoted
after a call · net
Company course
A dev team moving into agent work
As many developers as you need, on your shared codebase: the same method and conventions, so the whole team works the same way and closes the course with a shared pilot.
Quoted
after a call · net
You pay once; the method and what you walk out with stay yours. We set the rate to scope, the number of participants and your stack after one call about your repo and goal. If the course is not for you, we say so before any quote.
Who it is for, and who it is not.
This is about readiness rather than skill level. If the second column describes you, another path usually fits better, and we will say so on the call before you ever see a quote. We would rather send you where it actually helps than sell a course that will not change how you work.
Ready if
- You build software and want a repeatable agent harness
- You have a real repo to work on, new or inherited legacy, beyond curiosity
- You want output you can trust, with specs, tests and a trace
- You will keep building after the course instead of handing it off
Probably not, if
- You want it built for you: start with the free scan and our builds
- You need AI literacy for a non-technical team: see AI training
- You want a no-code promise or a certificate for the wall
- You expect production hardening, ownership and SLA from a course
Or we build it with you
Rather have it built and run in production?
The course teaches the method. Production hardening, ownership and maintenance stay a paid build. If you would rather not do it yourself, start with the free process scan, and we will tell you straight what is worth building, and whether it is worth building at all.
From a call to a working harness.
The course starts with one call about your repo, your stack and your goal. We run the rest in a clear order.
- 01A call about your repo, stack and goal: we set the format and scope.
- 02You get a quote and a plan fitted to your codebase.
- 03Working sessions on your real repo, online, building the harness.
- 04You keep the harness, the templates, the way of working and a pilot done the new way on your repo.
Questions about the course
Why pay for a course when I could figure this out myself?
The question is how many months it takes, and how many commits you ship to production without trusting them along the way.I already tried agents and rewrote the code anyway. How is this different?
That is exactly what the course is for. We do not show you better prompts; we show you a way of working where the agent stays inside bounds you set and its output gets checked before you trust it. The starting point is the very code that burned you.Won't I learn something that is stale in six months?
We teach the method rather than one tool's configuration. Models and plugins change; the way you set bounds for an agent and verify the result stays. We show it in your codebase and you close with a pilot, so on Monday you go back to code where it already works instead of a blank page.Why don't you list a price?
Because the scope varies too much: 1:1 on your repo is different work than a company course on shared code. You get a concrete number after one call about the repo and the goal; we do not invent it from a price list.Which stack and repo do we work on?
The course assumes you have a repo and a real goal. If the project is only starting, the first session can set it up, but curiosity without a project is not enough.We have old code nobody wants to touch. Does the course still apply?
Yes, an old repo is often the best starting point. An agent is the best modernization assistant available today: first it reads the inherited code and describes how it really works, including the integrations nobody remembers anymore. Then it compares the docs against the code, adds verification, and only then writes new features. The project moves from “I don't trust it” to “documented and checkable”, and in the course we do this on your code, not on a sample.Claude, or other models?
We work most on Claude and Claude Code, because that is what we build production on, but we also teach on Codex (CLI and app), ChatGPT Work, OpenCode and other models if that is what you use. The method transfers between them, because it lives in specs, docs and evals, not in one tool. We say plainly where another model or tool fits better, with no false claim that one is always best.Is it a company course or 1:1?
Both. A 1:1 course for a founder, or a company course with as many developers as you need, on shared code and with a shared pilot at the end. We set the format with you before the quote.What do you need to start?
A repo and a goal. We set up the environment and tooling as part of the course, tuned to your stack. You do not need anything special before we begin.What do you keep afterwards?
The method in your codebase, the way of working and a pilot project done with it. It stays with you instead of on our slides.
Tell us your repo and goal, and we will send you the plan and the quote.
- 30 minutes with the engineer who would build it, not a salesperson.
- A review of the processes that cost you the most time and money.
- A written summary: what to automate, in what order, with cost ranges.
Times are shown in your own time zone. We work with clients across time zones.
No sales deck and no obligations. If automation doesn't make sense, we'll write that too.
Quoted per scope · led by engineers with systems in production