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Agile transformed how organisations build and deliver software. Its values and principles remain sound. For planned, human-led delivery, it is still the right model. But it was written for a world where the team was entirely human. That world no longer exists. A new class of work is emerging alongside traditional delivery: agentic development, where humans and AI work in parallel to compress timelines, reduce toil, and turn intent into working systems faster than any sprint cadence allows. This isn’t a replacement for Agile. It’s a second stream running alongside it, and most organisations have no framework for it. ...

There’s a moment that changes things. You’re sitting with an engineer from your team, looking at something on a screen. Not a slide deck. Not a roadmap. A working system. One that solves a problem you’ve been circling for months. You built the intent. The agentic workforce built the scaffold. The engineer made it real. And somewhere in that conversation, across those few hours, something that had been sitting in the back of your mind for years stopped being theoretical. ...

The Normalisation Risk Taking on more and making it normal? What starts out as good intentions, without explicit guardrails slowly rises the baseline. And like rising waters, it can gradually result in individuals treading water, trying to keep up with the world they’ve created and the renewed demands to keep improving it. Here’s an example that might sound familiar: A co-worker leaves and everyone else around steps up and does 20% more - they are proud of their work and their company, and they don’t want either to fail. But if the backfill isn’t replaced, then 120% gets normalised, and the backfill quietly gets deprioritised if those working 120% (or advocates for them) quietly accept their fate. ...

We are in a phase of AI where organisations are rolling out approved tools for all staff, and giving permission to use them to optimise their day to day. It’s great to see agentic workflows being adopted by many — technical and non-technical alike. Agile delivery teams are incorporating AI across their SDLCs, and it’s exciting to see the innovations emerging: agentic assessment of delivery plans against the target state architecture, high fidelity wireframes turning from design into code, user stories feeding to agents which analyse and produce code for review, test coverage increasing its reach, and incidents being triaged and reported to stakeholders without needing to interrupt those actually fixing the problem. Proving the actual productivity uplift turns out to be difficult on large scale projects, when the Time gained is quietly absorbed back into the project. ...