AI exposes bottlenecks in manual review, vague requirements and outdated validation models.
This article explains how spec-driven development, architectural truth models and agentic CI/CD pipelines reshape delivery for the AI era.
Agentic AI is changing how we design and build software, but not in the simplistic “AI replaces everyone” way some people imagine. It shifts where human expertise matters, compresses delivery time, and exposes which organisations are truly agile. This post looks at how roles evolve, where the real leverage is, and why communication becomes the core skill.
Every anti-pattern in this post felt reasonable in the moment. Nobody skips the spec because they’re lazy — they skip it because the deadline is close and the AI is right there waiting. The failures that follow are predictable. Here’s how to recognise and prevent them.
A single AI conversation works for focused tasks. When a feature spans backend, frontend, security, and infrastructure, it starts to fall apart. The multi-agent model gives you something different: a cross-functional team — architect, security expert, pentester, DevOps — all working on your feature at once.
The difference between mediocre and great results from agentic AI is almost entirely in how you communicate — not what you ask for. Here’s the daily workflow that separates teams that struggle from teams that genuinely benefit.
The fastest way to waste time with agentic AI is telling it what to build without telling it what you mean. The fix isn’t a better prompt. It’s a spec. Here’s the seven-step lifecycle that consistently produces good results.
If three teams can’t agree what a ‘Customer’ is, your agentic AI will industrialise that confusion across every artefact it produces — at speed. The fix isn’t a better prompt. It’s a definition.
Ask three teams what a ‘Customer’ is and you’ll get five answers. Add AI to that equation and you don’t get clarity — you get ambiguity at machine speed, embedded across every artefact the AI produces. The fix isn’t a better prompt. It’s a definition.
Most developers skip the briefing. They open a session, fire off a task, and wonder why the output doesn’t match how their team actually works. The fix is simpler than it looks — and you only have to do it once.
Part 1 of The Agentic AI Playbook Series Navigation: Most people who say they’ve tried AI-assisted development haven’t tried agentic AI. They’ve tried a smarter autocomplete. It’s a completely different thing, and conflating the two is the reason so many teams write off the technology before they’ve understood it. GitHub Copilot suggests a line. You accept or reject.… Read More »