This guide provides a technical-operational framework for platform administrators, AI architects, and delivery managers who are responsible for running AI agents, AI skills, and agentic workflows on ServiceNow. It covers five areas:

• how assists are consumed and billed,
• how to track and monitor that consumption,
• how to configure rate limits and safeguards,
• how to measure per-agent accuracy,
• and how to run automated evaluations before and after production deployment.

Content in this guide reflects the Australia release (including Australia Patch 3, June 2026) and references system properties, tables, and dashboards available in Now Assist AI Agents plugin version 6.0 and later. Where a feature requires a specific patch level, that requirement is noted inline.

This document is intended to be used alongside the official ServiceNow product documentation and the AI Center of Excellence community articles. It consolidates scattered guidance into a single operational reference.

Table of Contents

Purpose and scope 4

Understanding assists consumption 5

    Assists vs. actions 5

    Agentic workflow tier sizing 5

    Key tables for consumption data 5

Tracking and monitoring assists 6

    Now Assist Analytics 6

    Subscription Management: the cross-instance view 8

    Assist spike alerting 8

    Building a custom forecasting dashboard 8

    Operational pro tips 9

Logging and auditability 10

    What the platform logs automatically 10

    Tracing an assist charge to a business record 10

    Logging best practices 10

    Aggregation and reporting 11

Rate limiting AI 12

    One Extend rate limit rules 12

    Trigger throttling (Australia Patch 3+) 12

    Recursive check properties 13

    Agent design patterns that reduce rate limit pressure 14

Measuring accuracy per agent 15

    Core evaluation metrics 15

    The Agent Productivity Score 15

    The Workflow Automation Score 16

    Accuracy tracking cadence 16

    Per-agent dashboard design 16

Automated evaluation practices 17

    The evaluation development cycle 17

    Building evaluation datasets 17

    AI Agent Advisor: opportunity discovery and pre-built datasets 17

    Connecting evaluations to analytics 18

    A/B testing for model and prompt changes 18

Governance with AI Control Tower 18

Implementation checklist 18

    Week 1–2: Foundation 18

    Week 3–4: Monitoring 19

    Week 5–6: Accuracy and evaluation 19

    Week 7–8: Governance 19

    Ongoing 19

Appendix: quick reference tables 20

    Key system properties (sn_aia_property) 20

    Key platform tables 21