⛔ STOP being theoretical about Agentic AI governance and make it practical
Decision Governance ensures operationalizing governance does not become an enterprise-wide overhaul.
This is how governance becomes operational instead of theoretical 👇
Step 1️⃣:
Identify high-risk actions:
* Approvals
* Payments
* Discounts
* Customer communications
* Policy overrides
Step 2️⃣:
Put an executable decision behind those actions.
Example:
“Can this agent approve this refund in this context?”
Step 3️⃣:
Make the decision explainable using decision modeling:
* What policy was applied?
* What data was used?
* Why was this allowed or blocked?
Step 4️⃣:
Separate workflow from governance.
The agent handles the workflow.
Governance rules:
• Decision Control: Should this action be allowed? Under what condition?
• Decision Traceability: What decisions led to this action?
Make governance implementation:
✅ Practical
✅ Iterative
✅ Risk-driven
✅ Measurable
👉 Read more at https://lnkd.in/gPPhHNSa
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Posted here.
Published July 28th, 2026 at 07:30 am
