4 AI Native Developer Patterns

Abstract

Development is experiencing a new phase of automation, similar to what we saw with DevOps. Numerous new tools are emerging, and it can be challenging to keep up with them. These tools, many empowered by agentic AI, are leading to new practices, and understanding the patterns of these practices will help you navigate the space of AI Native Development.

We'll dive in the following 4 patterns (backed with tool examples) :

  1. Producer → Manager : less typing, more reviewing and steering
  2. Implementation → Intent : describe the goal, let AI build
  3. Delivery → Discovery : test more ideas, faster
  4. Content → Knowledge : expertise becomes the edge, not just code

AI isn’t replacing developers : it’s changing where we create value. Conway's law highlights the socio-technical impact of tools. We'll go beyond the individual impact and highlight how teams and organizations might change in this new agentic world.


Speaker

Patrick Debois

AI Product Engineer @Tessl, Co-Author of the "DevOps Handbook", Content Curator at AI Native Developer Community

Patrick Debois is a practitioner, researcher, and eternal learner exploring how AI agents are reshaping software development — not just for individuals, but for teams and organizations. As Product DevRel lead at Tessl and curator of ainativedev.io, he studies AI-native development patterns, context engineering, and how the context flywheel turns everyday coding into organizational knowledge. He organizes AI Native DevCon and is a frequent conference speaker known for structured, succinct talks. From DevOps to DevSecOps to AI-native dev — Patrick has been at the frontier of emerging practices, always drawn to the same question: how do teams get better, together?

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