Three small quality gates that make AI coding workflows cheaper and safer

A pattern I keep seeing: teams add an AI coding tool, get a burst of speed, and then quietly lose time on rollback, retesting, and noisy diffs.

The fix usually is not a bigger model. It is three boring gates:

1. A preflight gate before generation.
   Ask: what files can change, what tests must pass, and what budget should this task stay under? A 30-second checklist prevents most oversized runs.

2. A validation gate after generation.
   Run lint, unit tests, and one focused regression check tied to the task. If the agent changed auth, billing, or deploy code, add one explicit safety check instead of trusting a green summary.

3. A cost gate after merge.
   Track token spend and rerun rate per task class. In practice, cheap models often win on narrow refactors, while expensive models only pay off on ambiguous multi-file work.

One simple rule of thumb: route easy edits to the cheapest model that can keep structure, and reserve premium models for planning, debugging, and risky migrations.

The interesting part is cultural: once people see cost, retry rate, and escaped bugs on the same board, prompts get shorter and handoff quality gets better.

Curious what gate catches the most failures for your team today: preflight, validation, or cost review?