AI does not fail only because it is unreliable. It often fails because we ask it to fulfill...
AI does not fail only because it is unreliable.
It often fails because we ask it to fulfill expectations we never explained.
That is the core of spec-driven development for me.
Not process. Not documentation theater. Not a return to slow software delivery.
Spec-driven development is the practice of giving AI the information it needs to do the work correctly: purpose, context, boundaries, constraints, architecture, business expectations, acceptance criteria, and review gates.
Why most AI agents disappoint in production (and what to fix first)
Vibe Thinking - The PM Who Writes Requirements That an AI Can Actually Use | Towards AI
AI still needs humans
Why AI Works Best When It Works with Humans - SPONSOR CONTENT FROM AWS AND EFFECTUAL
TL;DR: AI coding fails because context is temporary and intent is vague. OpenSpec introduces...
What SDD is, when it works, where it fails, token economics, and practical adoption patterns for AI-assisted software development.
I spent the last few years running QA, across teams. The same structured process worked, but only...
I’ve been thinking a lot about a failure mode in AI-assisted development that does not fit neatly...
This is the second post in a series about spec-driven development from a practitioner who has been...
Agents disappoint in production when we ask them to drive on roads built for dashboards. Build these four guarantees into the…
