AI agents should not just generate code.
They should produce evidence.
That is the core idea behind Agile V:
→ Requirements before implementation
→ Independent verification
→ Traceability from intent to test
→ Human gates before release
→ Evidence bundles instead of after-the-fact documentation
Agent skills for traceable requirements, independent Red Team verification, human gates, and compliance-ready evidence.
→ github.com/Agile-V/agile_v_skills 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.
source & further reading
dev.to — original article
Bayesian Neural Networks
Which LLM should I actually code with? I built a small benchmark to find out
Detecta si tu modelo de materiales hace trampa con la 'huella bibliográfica'