Best Practices for AI-Assisted Development of Distributed Systems

AI coding assistants are becoming part of everyday software delivery, but distributed systems are still difficult for them to understand. The problem is rarely the model alone. It is often the way we structure architecture decisions, specifications, tests, repositories, and integration code.

This session shows practical patterns for making complex systems easier for both humans and AI to reason about. We will cover how to keep architectural context close to the code using best practises for architecture specification, markdown-based documentation, conventions, and repository-level rules. We will also look at specification-driven development and AI-assisted TDD: how to keep system behaviour, tests, and implementation aligned so that generated code remains safe to change.

The talk will also discuss how repository structure, stack selection, and integration patterns affect AI-assisted development: token usage, context quality, boilerplate, pull request readability, and the ability of AI tools to understand system boundaries. The goal is to give attendees a practical framework for choosing tools and engineering practices that improve context awareness instead of simply generating more code.