A practical guide to navigating identity crisis and poor developer experience while trying to stay ahead of the curve of coding with AI agents. [read more] Agile AI Development Lifecycle At the end of 2025, AWS open-sourced their AI-DLC methodology. At first it looks like an easy-to-adopt (just drop markdown files into your repository) framework that should take one from pre-AI software development lifecycle to the AI-native future. In its nature it is very similar to compound engineering and superpowers - all of them are a collection of skills in markdown prose that rely on human-in-the-loop review. Notably both of them were introduced to the public a few months before AWS released AI-DLC, but it's hard to compete with AWS in terms of visibility. The AWS AI-DLC is designed to guide users through 30+ stages of development in total, grouped into 5 phases: initialization, ideation, inception, construction and operation. Every stage requires thorough mob-review of AI-generated artifacts. The pros of this approach are ease of adoption and the fact that it is designed by AWS. The cons are that it is essentially an automated waterfall (4 out of 5 phases produce documentation) with quality being defined by how thorough and knowledgeable the reviewers are. So unless your company is staffed by superhumans who enjoy reading tons of AI-generated documentation, you too might look for something else, especially if you need to scale the AI methodology outside of one engineer's machine working with a handful of repositories. Platform Engineering The second camp of AI adopters at scale doesn't have a single shared name for the methodology, because they didn't invent something new. They evolved their existing workflows and integrated them with AI capabilities. Notable publications include CNCF Platforms White Paper, Spotify's Golden Paths and Netflix's "paved roads". The core idea is to standardize their processes and tools to make it easier for the teams to execute on business...