Spec → Ship: A Practical Workflow for Building With AI Coding Agents

AI coding agents are great at producing output. The real challenge is producing outcomes: software that is correct, secure, and maintainable.

This is the workflow I use to get reliable results.

1) Start with constraints, not features

Before anything else, define:

Audience (who is this for?)
Success metrics (what improves?)
Non-goals (what’s explicitly out of scope?)
Risks (security, data integrity, performance, privacy)

Constraints reduce wasted effort and prevent “AI-driven scope creep.”

2) Make “done” testable

A good spec makes acceptance obvious. Examples:

“Admin routes require authenticated admin users.”
“Published content is visible publicly; drafts are not.”
“Build succeeds even when optional env vars are missing.”
“RSS and sitemap are generated.”

If you can’t tell whether it’s done, it’s not scoped.

3) Slice the work into verifiable increments

The agent should ship in small chunks:

one route,
one CRUD surface,
one policy layer,
one data fetch path.

Each slice should end with: it compiles, it runs, it matches the spec.

4) Put security rules where mistakes are hardest

Two principles:

Enforce permissions close to the data (e.g., row-level security).
Keep server-only secrets server-only (never in client bundles).

Even if your UI is “correct,” assume it can be bypassed.

5) Prefer boring architecture

When in doubt:

a clear table schema over “flexible JSON everywhere”
explicit server actions over complex client state
readable code over clever abstractions

Boring systems are easier to secure and maintain.

6) Add a “verification loop” to every change

A reliable loop looks like:

compile/build check
a quick click-path sanity check (the happy path)
at least one failure-mode check (unauthorized, missing data, empty state)

If you only test happy paths, production will test the rest.

7) Capture the decisions

The highest-leverage output isn’t code—it’s why the code is this way. Keep short notes:

what tradeoff you made
why you chose it
what you’d revisit later

That becomes your roadmap.

If you’re building with AI agents and want fewer surprises, this workflow is a strong default. If you want help applying it to your product, reach out via Contact.