governance2026-01-201 min read
Agentic AI Operating Model for Enterprises
A practical operating model for deploying agents safely—roles, controls, runbooks, and measurable outcomes.
title: Agentic AI Operating Model for Enterprises
description: A practical operating model for deploying agents safely—roles, controls, runbooks, and measurable outcomes.
date: 2026-01-20
tags: [governance, operating-model, enterprise, auditability]
Why an operating model matters
Most “agent demos” fail in production because ownership is unclear: who approves tools, who monitors runs, and who responds when an agent does the wrong thing?
The minimum viable operating model
1) Ownership
- Product owner: defines outcomes and success metrics.
- Platform owner: manages tools, routing, budgets, and guardrails.
- Risk owner: approves policies, audit controls, and data handling.
2) Controls that scale
- Policy-as-code for tool access and data scopes
- Cost caps per tenant / org / workflow
- Immutable run logs (inputs/outputs/tool calls)
3) Runbooks
- Incident response for “agent misfire”
- Prompt rollback / version pinning
- Vendor failover and routing policy updates
What to measure
- Task completion rate
- Human override rate
- Mean time to detect / resolve
- Cost per successful outcome
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