Nobody learned a city from a map
The fastest way to learn agentic development is to stop studying it: move in, extract repeatable patterns into skills, and reflect to compound.
The fastest way to learn agentic development is to stop studying it: move in, extract repeatable patterns into skills, and reflect to compound.
A mobile app, past its usefulness, was days from being phased out. One email reversed the decision. No discussion. No input from engineering. This is what happens when decision-making drifts too far from the work.
Developers don't skip standards because they're careless, they skip them because there are fifteen things to remember and the code was the hard part. The real question isn't which tasks your LLM handles well. It's what's still slipping through ungated.
A couple of months ago, I was copy-pasting prompts into ChatGPT. Now I'm shipping features, running tests, managing branches, and keeping documentation alive, with a team of agents doing the heavy lifting. All by myself.
Legacy codebases are messy, undocumented, and full of decisions nobody remembers making. But if you can explain it to a new developer, you can onboard an AI and that changes everything.
Software development's feedback loop has compressed from years to minutes, but QA remains the last bottleneck, the one place still dependent on human judgment. AI is rapidly closing that gap, and before the year is out, that final human checkpoint may no longer be necessary.
Agile was supposed to free us from bureaucracy. Many teams just rebuilt it with better branding. Now, AI-driven development is forcing the uncomfortable question: Were we ever truly agile, or just managing slow feedback loops?
AI is changing what small teams can ship, boilerplate gone, prototypes faster, experimentation cheaper. But lower costs of building don't mean lower costs of building the wrong thing. It just means you can do it faster.
AI made writing code faster, but the real economics of software engineering were never about typing code in the first place.