Best Practices in managing AI agents, skills, and assists in ServiceNow

hussein_alani · an hour ago

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