Webinar recording · 8 October 2026

Coding got faster.
Delivery didn't.

AI made coding fast, but the rest of delivery still moves at human speed. Pasi Vuorio shows what comes next in agentic engineering, with a live demo of the Agentic Engineering OS on a real, open-source codebase.

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With Pasi Vuorio, Founder & CEO, ModernPath · 63 min

Chapters

Jump to the part you need

The live demo starts at 40:04. The story of one customer question, before and after ModernPath, runs from 9:47.

“Everything that we do is mapped to the requirement ledger. Whatever decisions are made, everything is stored.”
Pasi Vuorio, Founder & CEO, ModernPath
Pasi Vuorio, Founder & CEO, ModernPath

Your host

Pasi Vuorio

Founder & CEO, ModernPath

A repeat founder and veteran AI-native software leader with hands-on deep AI engineering experience. Pasi advises from the C-suite to individual developers. He runs the full session: why the bottleneck moved, what requirement-driven development changes, and the live demo on a real codebase.

In the recording

From one fast developer to a fast team

Agents write more code than people can review, and work piles up before and after it. Teams went from vibe coding (one developer, one agent) to spec-driven development (plans before code), but it is still one developer at a time while the rest of the team works the old way.

What comes next is requirement-driven development: the whole team on one flow, with requirements, human gates, and proof on a shared map of the system. The engineering OS makes this work by solving the 3 things that break when AI-assisted delivery scales.

Context

The Knowledge Core reads your existing system, so agents work from how it actually works instead of guessing.

Trust

The Ledger traces every requirement to its tests and code, both ways, so you know what was built, why, and whether it is safe.

Scale

The platform runs the process, and Mission Control brings you only the decisions that need a human.

The platform sits above the coding tools your developers already use, such as Claude Code, Cursor, Codex, and Copilot, and drives them with your context, rules, and acceptance criteria. Running it on ourselves first took our release cycle from 3 months to 1, with the same team and the same quality bar.

Asked live

Questions from the session

Is there assistance for architectural work, such as separating the code into a clean separation of concerns?

Asked by a participant · answered by Pasi at 1:00:10

Yes. Engineering constraints are the architectural and non-functional requirements that every change is validated against, so the code the platform produces complies with the constraints set for the system. Architectural patterns documented in the Knowledge Core also guide how the AI decides to do things, and are checked to be applied.

Other questions were answered by the team in the chat during the session.

After the recording

Go one step further

Two narrated, interactive presentations by Pasi. Press V for narration, or click through at your own pace. Best on a desktop browser.

The webinar slides

The same story as the recording, as an animated presentation with AI narration. About 28 minutes, or skim it in 5.

Open the slides ↗

Find your path

The product presentation, about 15 minutes. Pick your situation: legacy is slowing you down, your code is in good shape and you want AI speed, or you are building something new.

Open the presentation ↗

See it on your codebase

A personal demo with the team, on a system like yours and with your questions.

Book a demo →

Who it is for

Built for people accountable for scaling AI development at the organization level, not for individual developer speed. As Pasi puts it in the recording: with one developer the platform adds little; from 2 or 3 developers on a project, it adds the structure that speeds the team up.

CTOs and Engineering Leaders

“Somewhere along the way I became the coordination engine for my own team.”

Architects

“I need to see the shape of the system before I trust an agent to change it.”

Lead and Principal Engineers

“Hand-feeding context to an agent does not scale on a legacy codebase.”

We built ModernPath after 1.5 years of AI-native delivery, and we ran it on ourselves first.

20+

Production apps shipped

3x

Faster release cycle, same team and quality bar

10x

Less human effort in our own delivery

Want to see it on your own codebase?

Book a personal demo. We show the platform on a system like yours and take the questions the recording could not answer: data residency, multi-repo codebases, and agents on legacy code.

Book a demoNot ready? See how the platform works →