AI coding is not the hard part anymore.
AI can already generate code quickly.
That is no longer the real bottleneck.
The harder question is:
Can we trust what was generated?
In traditional engineering, we do not accept a change just because someone wrote it. We ask:
- What requirement does it satisfy?
- What assumptions were made?
- What tests prove it works?
- What risks remain?
- Who reviewed and approved it?
AI-generated work should be held to the same standard.
That is the idea behind Agile V.
Agile V brings structure into AI-assisted development
By connecting:
→ Intent
→ Requirements
→ Implementation
→ Tests
→ Traceability
→ Evidence
→ Human approval
The goal is not to slow AI down.
The goal is to make AI-generated engineering work reviewable, repeatable, and trustworthy.
Because in real products, "the agent said it is done" is not enough.
Explore the projects
agile-v-skills
Agent skills for traceable requirements, independent Red Team verification, human gates, and compliance-ready evidence.
→ github.com/Agile-V/agile_v_skills
agentic-agile-v
A practical scaffold for running AI engineering with structured briefs, evidence bundles, validation gates, and risk-based workflows.
→ github.com/Agile-V/agentic_agile_v
From vibe coding to verified engineering.
