Methodology

Coding agents implement specifications well and tickets badly. This guide applies spec-driven development to agent-produced code: the template, acceptance criteria that become verification gates, the spec-to-verified-change loop, and what good specs look like.

Every prompt edit, model update, retrieval change, and tool change can move quality in ways nobody predicted. AI regression testing gates each one on a golden set with category-level thresholds, so improvements ship and silent regressions do not.

Demo calls prove nothing. Evaluating a voice agent means measuring task completion, containment, latency, turn-taking, transcription accuracy, and compliance behavior against a scripted call set, in shadow and pilot, with sign-off criteria agreed before the first live call.

How does FISTA actually deliver AI? The process—discovery, spec-driven build, evaluation, adoption, and documented handoff—that turns AI projects into shipped systems.

The theory of spec-driven development is simple; the practice is where reliability is won. How a specification shapes real AI delivery, step by step.

You can't unit-test probability the old way. How AI quality assurance—evaluation, adversarial testing, and monitoring—keeps AI systems reliable in the real world.

A good AI project is won or lost in scoping. How FISTA's discovery defines the outcome, assesses data reality, and surfaces risks—before a line of code.

In the AI era, the scarce skill isn't producing output—it's verifying it. How verification-led engineering makes AI reliability provable, not hopeful.

AI projects don't run like traditional software projects. The differences—uncertainty, data, evaluation gates, adoption—and how to manage them without nasty surprises.