As global AI adoption has accelerated through the first quarter of 2026, it's become clearer where the baseline bets that you need to make as a modern engineering organisation need to be on the curve of AI adoption in your day-to-day workflows.
It's no longer a question of "can these tools work" (they can and do) – software professionals using AI tooling is the new normal - the question now moves to be "what are the most effective ways we can use this new category of tooling to speed up where we need to go".
Through a purely technical lens, our goals are clear – we want to build increasingly stable, resilient systems, reduce complexity at every opportunity, and ensure that the platforms we build can operate at a competitive cost in the market.
These are not our only software goals (they exclude product direction; they don't speak to meeting the future of the technology landscape) but as we consider what our baseline set of AI capabilities should be across our systems they are the bedrock on which we build.
The current state of the art in AI models and tooling is accelerating our ability to reason about complicated distributed systems as one cohesive whole, and this document outlines techniques, and subsequently highlights a direction of travel to take advantage of this change in the technology landscape.
