Building software has never been so easy, and yet leading technology has never been so hard.

• The velocity of execution requires a review of the more fundamental principles of how the technology organisation operates.
• The expectation from investors has skyrocketed.
• And if you are not an AI-native business, you have pressure to understand how to compete in the “AI eats the world” phase.

It feels daunting. And things keep changing. Where to start from?

The principles you need were written down twenty years ago. Most teams ignored them because they could get away with it. Agents have removed that escape route.

For many teams and organisations, the pillars were:

• Planning: you need to understand what to do and what the cost would be, as you can fit a limited number of experiments in any given time. Planning will enable throughput and consistent delivery.
• Total Cost of Ownership: what’s the overall cost of this initiative? In particular, will this have a long tail of micro work that will eat away precious planning slots?
• Quality: manual inspection via code reviews, testing, human judgment was how you kept standards high. At human-produced volumes, it was imperfect but just about sufficient.

If you could manage these three steps, you would be well placed to achieve your goals.

But the landscape has changed. When the cost of writing software is shrinking, and the time to execute shrinks from days to hours, what is the north star?

Today, what matters is:

• Clarity on the goal: as execution time shrinks, clarity on the goal is more important than ever. Run more experiments than ever before, but be ruthless about which ones graduate. Small steps are great for agents and for focus: smaller prompts, tighter scope, better results. Repeat.
• Repeatable feedback loops for sustained velocity and sustained progress: your agentic coding flow is 10x better if you empower agents with a deterministic test. It’s teaching your agent what the final goal is, not letting the agent guess it. What was (wrongly) often seen as an unnecessary burden in the past is now a hard requirement, or the precious tokens will be gone.
• Observability is what enables quality and sustains velocity. Code reviews have always been more of a confidence mechanism, not an effective quality raiser. It worked well enough when humans wrote code slowly enough to read it. We passed that stage. Furthermore, the bugs that actually hurt you in production were never visible in a diff. Everyone is now pushed towards observability, a mechanism that many teams should have trusted more all along. Finally, models are exceptional at identifying complex incident scenarios — making observability the connective tissue between production reality and agentic incident response.

The agentic revolution, by stripping away the implementation bottleneck, is a forcing function for how teams and businesses operate. Old operating models were built on assembly line logic — optimise each function locally, manage handoffs between them. In the agentic era that breaks entirely: velocity without flow is just a faster queue. Throughput — the number of ideas that complete the full journey from idea to monetisation — only improves when the whole system moves together. The businesses truly blooming are not the ones running multiple agents in a Ralph Wiggum loop; they are the ones adopting AI end-to-end.

Agile (capital “A”) has been dead for years. Yet, AI can be the best agile transformation you have ever experienced.