The agentic platform defines what needs to be provided. Team Topologies defines who provides it, and how teams interact to make it happen. This second article applies Skelton and Pais’s model to a world where agents produce and humans orchestrate.

The agentic platform defines what needs to be provided. Team Topologies defines who provides it, and how teams interact to make it happen.

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⚠️ This article is now obsolete and is replaced by a human-reviewed version here ⚠️

EDIT: Author’s Note & Hacker News Aftermath: This article was originally written for a French audience, and I used an AI ghostwriter to translate and adapt it for English readers. As the Hacker News community rightly pointed out, this resulted in a dense, “word-salad” reading experience. However, my main goal was to share a foundational idea to spark debate—and that debate absolutely happened. I am leaving this original text unedited to preserve the context of the HN discussion, but I want to thank the community for their candor. Below is a summary of the very real, open engineering questions raised by the readers.

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In the first article of this series, we asked the what: which systemic capabilities (context, guardrails, tooling) are needed to produce reliable applications at scale. The answer was the agentic platform, and at its core, the agentic factory: the mechanism where agents plan, code, test, and ship.

But a platform does not build itself, and more importantly, it is not consumed the same way it is built. A fundamental question remains: who does what?

Generative AI has transformed how code is produced. In just a few months, we went from autocomplete to agents capable of writing, testing, and deploying entire applications. The market is now flooded with methods for framing these agents and making them produce quality code.

But this abundance raises a question few organizations are asking yet: what happens when you are not building one app, but fifty?

An out-of-the-box code agent only sees a repo and a shell. This article lays out three invariant principles for turning a gadget into a production tool.

How I built a multi-agent system in Go to generate Google Slides presentations from pre-formatted templates (not just to present, but to convince). A hands-on account illustrating the principles of the agentic mesh.

Convictions for designing tomorrow’s agentic systems — an agent mesh system capable of delivering value at scale, borrowing principles from the data mesh.

Note: this article has been translated by an AI; original is in French

Introduction

I have always been fascinated by AlphaGo, then AlphaZero, and this concept of reinforcement learning. A machine that plays against itself, learns on its own, and reaches superhuman levels. There is something magical there that goes beyond mere technical prowess.

When in 2016, AlphaGo defeated Lee Sedol, one of the greatest Go players of all time, it wasn’t simply a technological victory. It was a pivotal moment in our relationship with artificial intelligence. For the first time, a machine demonstrated its ability to master a game considered the pinnacle of human intuition, a domain where experience and creativity seemed irreplaceable.