2 posts tagged with "governance"

Most teams building AI agents spend the first month chasing performance: better prompts, smarter routing, faster retrieval. They spend the next six months chasing the thing they skipped—governance. Agents that can't be audited get shut down by legal. Agents without permission boundaries wreak havoc in staging. Agents without human escalation paths quietly make consequential mistakes at scale.

The uncomfortable truth is that most agent deployments fail not because the model underperforms, but because the scaffolding around it lacks structure. Nearly two-thirds of organizations are experimenting with agents; fewer than one in four have successfully scaled to production. The gap isn't model quality. It's governance.

For most of AI's history, the governance problem was fundamentally about outputs: a model says something wrong, offensive, or confidential. That's bad, but it's contained. The blast radius is limited to whoever reads the output.

Agentic AI breaks this assumption entirely. When an agent can call APIs, write to databases, send emails, and spawn sub-agents — the question is no longer just "what did it say?" but "what did it do, to what systems, on whose behalf, and can we undo it?" Nearly 70% of enterprises already run agents in production, but most of those agents operate outside traditional identity and access management controls, making them invisible, overprivileged, and unaudited.