Why specs and agents fit
AI agents do best with clear, durable context, and a specification is exactly that: a written statement of what to build and why. It gives the agent a fixed target instead of an evolving chat to infer from.
Spec-driven development was good practice before; with agents it becomes a force multiplier.
Spec first, code second
Writing the spec first forces vague intent into concrete, testable requirements. That clarity is what lets an agent implement the right thing rather than a plausible-looking wrong thing.
It also surfaces ambiguities while they're cheap to resolve — before any code exists.
Specs reduce drift
Because the spec is a stable reference the agent returns to, it counteracts the session drift that comes from a growing, lossy conversation. The requirements don't fall out of the context window.
The spec is the anchor; the chat is just the working surface.
Plan and tasks from the spec
From a spec you derive a plan (how) and a task list (steps). Breaking work into spec-grounded tasks keeps large features coherent across many agent sessions.
Each task carries its slice of the spec as context, so the agent stays aligned end to end.
Reviewable by design
A spec gives you criteria to check the output against: did it meet the requirements? That makes AI-generated work reviewable instead of judged on vibes.
Clear acceptance criteria turn 'looks fine' into 'meets the spec'.