2026¶

May 13, 2026
in llm-d, Deep Dives
10 min read

From Simulation to Production: How an AI-Native Pipeline Discovered a Better Admission Controller for llm-d

A case study in closing the AI-native loop: observe, reason, change, validate, deploy.

Introduction

An AI-native system is one that continuously and autonomously closes the loop from observation to action to deployment, with AI as the primary agent driving this process. Rather than humans manually directing each improvement, humans establish objectives and boundaries while the system autonomously executes the cycle, at machine speed.

April 21, 2026
in Vision
13 min read

AI Native Systems: Autonomous Evolution at Machine Speed

Tamar Eilam, Fabio Oliveira, Michael Factor

1. Introduction: The Bottleneck in System Evolution and AI Native Systems

Modern software systems, especially those that serve AI workloads, are extraordinarily complex and must evolve continuously under pressure from new models, new hardware, changing usage patterns, and shifting business objectives. These pressures drive constant change, both in configuration and in code. Yet, even with increasingly powerful AI tools, improvement of such systems remains fundamentally human-driven. Engineers inspect logs and metrics, diagnose problems, open tickets, draft and review pull requests, extend tests, and orchestrate deployments through fragmented workflows. AI assists at each step, but progress is mediated by people, one decision at a time.