Coding agents make a big difference to how individual developers do their job. Some activities, in particular those relating to the production of code, can be a whole lot faster.

Coding agents also make a big difference to how we run engineering organisations, because they have traditionally been shaped by the constraint that code was very expensive to produce. Examples of how organisation-level processes change include how we treat legacy code, how we design architecture and how we decide whether to buy something off-the-shelf or build it ourselves.

The changing economics of software will reshape the context within which every software developer works. If you understand these dynamics, you have a better chance to take advantage of the opportunities.

In the current AI landscape, systems treat every query as a blank slate, forcing them to rediscover information from scratch. The LLM Wiki pattern disrupts this by turning AI into a persistent knowledge architect. By transforming markdown tools like Obsidian into an active development environment, the AI treats a wiki as living source code, actively building and maintaining an evolving ecosystem of your data.

This shift replaces static, siloed documents with composite knowledge. As you add information, the AI automatically handles the operational "dirty work": summarizing, labeling, cross-referencing, and resolving contradictions. Consequently, your knowledge base becomes richer and more valuable over time. By delegating these tasks to the AI, humans are freed from information management and empowered to focus entirely on high-level exploration, strategic synthesis and decision-making.

Most conversations about AI in software engineering focus on shipping features faster. But in a fast-moving environment, the real bottleneck often lives on the other side: the time between a reported issue and a confident resolution.
We'll share how a maintenance team and a delivery team, working on a core service with multiple consumers, spread across internal and external teams, investigating issues across multiple data sources, operate as one connected AI-first system.

With workflows designed around AI strengths: triage, data correlation, supporting cross-team communication, and handling context cleanly between maintenance and delivery.

You'll leave with concrete patterns for AI-first incident management, a model for maintenance-delivery collaboration that scales, and an honest view of where AI helps  and where it still gets in the way.