Blog
[9 posts]
Governing agents in the real world
Practical frameworks for inventory, policy, evidence, cost, and human review across the AI agents your organization operates.
What a 14-day AI agent audit should deliver
A useful agent audit does more than catalogue tools. It produces a verified inventory, evidence gaps, risk-ranked findings, and a control plan the team can act on.
AI agent cost visibility has to follow the run
A provider invoice tells you what you spent. Run-level attribution shows which agent, workflow, team, and outcome created the cost—and where to set controls.
Build the AI evidence pack before the deadline
EU AI Act readiness is not a last-minute document exercise. Connect inventory, ownership, risk decisions, monitoring, and human oversight while the system is running.
Risk-tier AI agents by authority, not intelligence
The smartest model is not always the riskiest system. Tier agents by what they can access, change, and affect—and by how reversible those effects are.
Human review without approval theater
More approval prompts do not create more control. Good oversight routes the right decisions to the right person with enough evidence to act.
Before you write another AI policy, build an agent inventory
You cannot govern an agent surface you cannot see. A useful inventory connects ownership, authority, data, tools, cost, and lifecycle in one operating view.
What an AI agent audit trail must capture
Application logs show that something ran. An agent audit trail must show who asked, what context was used, which policy allowed the action, and what changed.
From the OWASP Agentic Top 10 to operational controls
A risk list is useful. A control owner, enforcement point, and evidence record make it operational. Here is how to turn agent threats into day-to-day controls.
Govern AI agents before they govern your work
The moment an AI system can take action, governance has to move from model policy to operational control. Start with ownership, authority, and evidence.