The problem is not AI hallucination. The problem is missing evidence.
When AI makes a mistake, we often call it a hallucination.
But in engineering, the deeper issue is usually simpler:
There was no controlled process around the output.
- No clear requirement.
- No independent verification.
- No traceability.
- No risk record.
- No approval gate.
- No evidence bundle.
That means the problem is not only that AI can be wrong.
The problem is that teams often have no reliable way to prove when AI is right.
Agile V is designed around that gap
Instead of letting an AI agent jump directly from prompt to code, Agile V introduces a verified engineering loop:
- Clarify intent.
- Create traceable requirements.
- Build only against approved requirements.
- Generate tests independently.
- Red-team the result.
- Capture decisions and risks.
- Accept only with evidence.
This changes the role of AI
AI is no longer just a code generator.
It becomes part of a controlled engineering workflow.
That matters especially for regulated, safety-relevant, long-lived, or high-risk systems where correctness alone is not enough. You also need to explain how correctness was established.
Explore the projects
agile-v-skills
Agent skills for traceable requirements, independent Red Team verification, human gates, and compliance-ready evidence.
ā 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.
ā github.com/Agile-V/agentic_agile_v
From vibe coding to verified engineering.
