June 12, 2026 · 9 min read

AI Governance: Keeping Agents on a Leash

⏱️ 9 min · AI Governance · Ethics · Guardrails

🎯 Today's Focus

How to build trust in agentic AI — the governance framework every operator will ask about.

🛡️ The Governance Stack

Layer	Function	Implementation
Policy Engine	Hard constraints	Budget limits, SLA floors, security standards
Audit Trail	Accountability	Every agent decision logged, traceable
Human Override	Critical decisions	Escalation triggers for exceptions
Explainability	Transparency	Why did the agent choose this bid?
Fairness	Non-discrimination	All vendors treated equally by policy

🎯 The Human-in-the-Loop Model

Your Catalyst explicitly includes "human-in-the-loop oversight for critical decisions." Define what "critical" means:

Auto-execute: Standard provisioning, routine scaling, micro-transactions
Notify human: SLA changes, vendor switches, >$X transactions
Require approval: Security exceptions, policy overrides, contract amendments

      The 90-9-1 rule: 90% auto-execute, 9% notify, 1% require approval. This is the only way to scale.
    

💡 Key Insight

Governance is not a technical problem. It is a trust problem. Operators will not deploy agents they cannot explain to regulators, auditors, and their own boards. Your governance framework is as important as your orchestration engine.

← Prev Next →