Foundations

By demanding useful explanations, installing human failsafes, and requiring clear "nutrition labels" for our AI, we can begin to pry open the black box.

AI agents fail in production not because of bad architecture, but because we test them like traditional software. Complex 30-step workflows can't be tested—they must be reviewed like human work. This shift changes everything for legal and product teams.

The NIST framework provides the map, but fostering a true culture of responsibility is the journey.

IBM's framework begins with a reversibility assessment that determines which of three automation tiers applies to a given task.

The work proposes a five-layer architectural framework that embeds governance and security requirements throughout system design rather than treating them as separate concerns.

A new approach is needed, one that thinks in terms of dynamic spectrums rather than static boxes.

The rise of autonomous AI agents is fundamentally expanding the attack surface for zero-click exploits, creating new and unpredictable risks.

Agentic AI demands a different approach to governance—proactive, structured, layered.