Flagship: 2 days, Prague or online
Two days after which you run your team the way the job works now: you know which of the two 2027 shapes you are heading for, your review policy for agent-written code is on one page, and your spend and pipeline are rebuilt around AI.
The AI-Native Engineering Manager is a two-day workshop for engineering managers whose teams already work with AI and who now have to rebuild their own job around it. Companies boost their engineers with AI and skip the managers. The team gets faster, the manager gets quieter, and the manager is the one who has to lead by example. AI changed what an engineering manager does before 10am. This workshop teaches the new job, in two days, in Prague or from your desk.
Next open run: Mon–Tue 26–27 Oct 2026, Spaces Albatros, Na Perštýně 342/1, Praha 1 (Národní třída)Pick in person or online when you register.
42,000 CZK per seat. from 620,000 CZK in-house
Not for
Who it is for
The program
The engineering manager job is splitting into two funded shapes and one dead zone. You place yourself on the table (span, what you optimise, what breaks first, who funds it) using your own calendar, not your title. You leave with the three drift signals checked against your last two years.
Coordinating people was the old bottleneck. Agents produce more code than any team can responsibly read, so someone decides what gets deep review, what gets sampled and what ships on trust. In most orgs that call is made by default. You leave with a written read of how it is made in yours today, the input for module 3.
You write your team’s review policy for agent-generated code with explicit risk tiers, on one page. Then two peers try to break it. You leave with the document, the take-home artifact of these two days.
You price two of your own backlog items on the ELA Cost-Math Sheet with Czech dev-cost baselines: build days, review hours at the same rate, tokens, rework risk. Most managers price the tokens and skip the review hours. The decision holds in front of your CFO.
Planning through deploy redrawn for work agents do in parallel, with the first change scheduled for the sprint after you’re back. Then the conversation agents forced onto your desk: the span that drifted up “since we have Copilot”, and what you ask your director to change.
You rehearse one question for your director: over the next four quarters, is my scope going wider across teams or deeper into technical direction? Then you say which one you want. You leave with your next exec update built with AI, reviewed, ready to send.
Your span has not moved in two years while the team’s output has, and nobody has told you what that means.
You know which of the two funded shapes you are drifting toward, and you have asked your director the scope question before they answer it without you.
“Should we build this by hand or with AI?” gets decided by whoever shouts loudest.
You run the cost math in front of the team: dev-hours vs token spend vs review overhead, and the decision holds.
Agents produce more code than the team can read, and what gets reviewed is decided by whoever is tired that day.
A one-page review policy with risk tiers: what gets deep review, what gets sampled, what ships on trust. Written by you, in the room.
AI is bolted onto the old pipeline: same estimates, same review queue, doubled confusion.
Planning, estimation, review, and testing redesigned around what AI parallelises, first change dated.
Your exec update is a wall of Jira links nobody reads.
Your update is a data story built with AI in 30 minutes that gets your headcount ask approved.
Format
Book by 14 October and you get an extra 1:1 hour with Marian to build your own AI use case, worth 430 EUR.
Spaces Albatros, Na Perštýně 342/1, Praha 1 (Národní třída). Pick in person or online when you register.
Prices in CZK, excl. VAT. Published, not on request.
Faculty
Our team is unique. We are not consultants selling academic theories or empty certificates. They have built multiple unicorn companies and helped organizations scale from startups to scale-ups to established corporates.
In-house cohorts
No two organizations are the same. That is why our learning designers have crafted a selection of sessions focused on the key challenges faced by today's leaders. We work together to customize these sessions specifically to your needs. Our experts do the heavy lifting throughout the process and craft the program that is just right for your organization.
What nobody else offers
AI courses teach a manager to use the tools. This one starts with the question the tools raise: which shape of your job is funded in 2027, and which conversation to have before someone has it about you.
Module 3 is 90 minutes of writing your own one-page policy with risk tiers and having two peers attack it. Most teams make this call by default; you leave with it made by design.
Reforge, Stanford, Wharton and Kellogg teach “AI strategy”. None prices a specific feature from your backlog with real money attached. Module 4 does.
Management courses teach workflow, AI courses teach strategy; module 5 redraws planning-to-deploy for AI-native delivery and dates the first change.
The seat buys a cohort that meets again at the Prague meetup, a talk slot in front of 120+ leaders, and a network your next hire already sits in.
“Nobody calls a meeting to tell you your shape stopped being funded.”
Proof, honestly labeled
This is the first open run of this workshop. The scores and logos above come from the Academy programs it grew out of, not from this cohort. First-cohort outcomes replace this section, unedited.
Seats are limited to keep every room interactive. Register, or book a private cohort for your team.
