Authority Guide

How we increase speed with AI coding tools while protecting quality

An engineering approach that increases coding velocity while controlling defect cost.

It captures tool-agnostic process principles: prompt discipline, review boundaries, testing gates, and rollout safety.

Author: Figtures Engineering Team

Note: Cursor/Codex references are technical context only and do not imply official representation.

Signals We Optimize For

• Shorter lead time
• Review quality and consistency
• Controlled regression risk

Typical Engagement Shape

• Workflow audit + pilot team setup
• Prompt/review policy standardization
• CI quality gate and release guardrail adaptation

When It Is Not the Right Fit

• Teams without code review culture and no commitment to testing discipline.
• Projects demanding speed while refusing quality-control mechanisms.

When This Guide Is Useful

This guide is directly applicable when you need to balance speed and quality, make model/workflow choices with evidence, and reduce delivery risk.

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Frequently Asked Questions

Are AI coding tools risky for junior-heavy teams?+

They are manageable with proper review and testing gates. The risk is uncontrolled usage, not the tools themselves.

Can this approach be applied to an existing codebase?+

Yes. We usually start with a pilot module/squad and scale progressively.

Let's adapt this approach to your product

We can map this decision framework to your product scope and define the first sprint plan together.

Schedule Discovery Call