Agentic AI: Building and Scaling AI Agents on the Now Platform (Development & Use Cases)

Vaishnavi Lathk · ‎11-10-2025

Introduction

With the Yokohama Release, ServiceNow took a major leap forward by introducing Agentic AI — a framework that enables organisations to build, deploy, and orchestrate intelligent, autonomous AI Agents on the Now Platform.

These agents go beyond traditional chatbots or copilots. They are self-driven, context-aware, and can plan, reason, and act independently inside your ServiceNow environment.

Whether it’s resetting credentials, checking compliance, managing IT incidents, or coordinating multi-department workflows — AI Agents bring true automation intelligence to enterprise operations.

This article provides hands-on development guidelines, configuration steps, and practical use cases so you can start designing your own Agentic AI solutions.

🎯 Objectives

In this post, we’ll explore:

The AI Agent Control Tower process — how agents collaborate under governance
Prerequisites to use AI Agent Studio
Step-by-step development of AI Agents
Real-world use cases and testing methodology
AI Agent categories and deployment recommendations
Best practices and architecture patterns for scaling Agentic AI
Summary and references for deeper exploration

🧭 Understanding Agentic AI

Agentic AI represents a paradigm shift — from assistive intelligence (helping users with insights or suggestions) to autonomous intelligence (agents completing actions end-to-end).

Each AI Agent in ServiceNow is a digital specialist that:

Understands a defined goal or “mission.”
Plans the sequence of actions needed to reach it.
Executes tasks across multiple ServiceNow modules (and even external systems).
Evaluates outcomes, learns from feedback, and improves over time.

These agents are not isolated — they communicate, coordinate, and even collaborate via the AI Agent Fabric, which acts as a communication and orchestration layer.

🏗️ AI Agent Control Tower: The Command Center

The AI Agent Control Tower acts as the enterprise’s AI mission-control environment. It provides governance, monitoring, and insight into all agents running across your instance.

🔹 Core Functions

Function	Description
Orchestration	Oversees interactions among AI Agents, ensuring collaboration and preventing conflicts.
Governance	Enforces compliance, security, and ethical AI policies.
Monitoring	Tracks performance metrics (accuracy, latency, completion rates).
Lifecycle Management	Handles deployment, versioning, and retirement of agents.
Reporting & Analytics	Provides insight into automation impact and ROI.

The Control Tower connects seamlessly with ServiceNow’s Performance Analytics, Predictive Intelligence, and Now Assist Skill Kit, giving administrators real-time visibility into agent performance.

🧩 AI Agent Orchestrator

At the heart of Agentic AI is the Orchestrator — a meta-agent that coordinates other agents.
It acts like a digital manager, assigning tasks, sequencing actions, and ensuring the right agent executes the right task at the right time.

The Orchestrator Handles:

Task delegation: Mapping subtasks to appropriate AI Agents
Policy enforcement: Ensuring each agent acts within defined boundaries
Workflow context: Passing relevant data (e.g., incident ID, request type) between agents
Error management: Re-routing failed tasks or alerting human supervisors

For example, in an ITSM workflow:

The Orchestrator may trigger a “Diagnosis Agent” to analyze logs, a “Remediation Agent” to execute a fix, and a “Notifier Agent” to update stakeholders — all without manual input.

⚙️ Pre-requirements to use AI Agent Studio

To start building agents, ensure you have the following components and roles configured in your instance.

🔸 Required Components

AI Agent Studio – The central development hub for designing and managing AI Agents.
Now Assist Skill Kit – Introduced in the Xanadu release; coordinates and manages multiple AI Agents, acting as a bridge between GenAI and workflow automation.
AI Control Tower Module – Provides governance, analytics, and auditability of deployed agents.

🔸 Required Roles

Role	Description
`now_assist_panel_user`	Allows users to access Now Assist tools.
`sn_skill_builder.admin`	Grants admin access for building skills in Skill Kit.
`now.assist.creator`	Enables creation of new AI Agents.
`sn_aia.admin`	Full administrative privileges for AI Agent Studio.

🧠 Step-by-Step: Building an AI Agent

Below is the recommended process to create, test, and deploy AI Agents effectively.

Step 1 – Define a Use Case

Every AI Agent begins with a clear business problem.
Go to: Now Assist → AI Agent Studio → Manage Use Cases → New.

Define:

Objective and KPIs
Trigger event (manual, scheduled, or system-driven)
Expected outcome
Impacted stakeholders

Example Use Cases:

User Role Verifier – Automates user-role validation and compliance checks.
Project Portfolio Policy Agent – Ensures all projects comply with governance frameworks.
Incident Auto-Resolver – Diagnoses and remediates common IT incidents autonomously.

Step 2 – Design Your AI Agent

Within the use case, define your new AI Agent:

Give it a name, description, and goal statement.
Define inputs (data sources, APIs, tables) and outputs (actions, updates, notifications).
Assign tools (RAG, flow actions, subflows, or integration APIs).

Agents can use ServiceNow’s native components such as:

Flow Designer for logic control.
Decision Tables for rule evaluation.
Generative AI models for summarisation, recommendation, or classification.

Step 3 – Enhance Agent Capabilities

To increase intelligence, add specialised modules:

Knowledge Retrieval: Use RAG to access KB articles or documents.
Contextual Memory: Enable persistent memory to track previous interactions.
External Integrations: Connect to APIs like Azure OpenAI or Service Graph.
Feedback Loops: Allow agents to learn from success/failure metrics.

Step 4 – Test & Validate

Before deployment, use simulation mode to test real-world scenarios.

✅ Validation Checklist:

Does the agent correctly interpret the input prompt or trigger?
Are all required permissions configured?
Are data access boundaries enforced?
Is latency within acceptable limits (< 2 seconds typical target)?

ServiceNow’s Test Run Dashboard provides granular logs and success metrics for each workflow execution.

Step 5 – Deploy via AI Control Tower

Once validated, publish your agent to production.
From the AI Control Tower, you can:

Monitor execution frequency and success rate.
Track policy compliance.
View agent health dashboards.
Pause, upgrade, or deprecate older versions.

Step 6 – Monitor & Continuously Improve

Use Performance Analytics and Predictive Intelligence to monitor trends, identify bottlenecks, and fine-tune agent behavior.

Encourage user feedback — AI Agents improve significantly when continuously trained on real interaction data.

💡 Example Use Cases (Hands-On Scenarios)

Use Case	Description	Key Tools Used	Outcome
1. Get User Roles	Fetches and validates user roles in real time.	Flow Designer, Scripted REST API, RAG	80% faster role validation.
2. Policy Compliance Checker	Checks portfolio data against GxP/ISO standards.	Decision Tables, Predictive Intelligence	Reduced compliance breaches by 30%.
3. Incident Auto-Resolver	Diagnoses incidents using logs and CMDB context.	Log Analysis API, AI Search, Now Assist	40% decrease in MTTR.
4. Employee Onboarding Agent	Automates account setup, training, and equipment assignment.	HRSD, Flow Designer, Agent Orchestrator	Fully hands-free onboarding.

🗂️ AI Agent Categories

ServiceNow classifies AI Agents into three main categories for scalability and governance.

Application-Based Agents
- Focused on a specific ServiceNow application (ITSM, HRSD, CSM).
- Example: Incident Resolver Agent, HR Benefits Agent.
Platform-Based Agents
- Built on core Now Platform services such as Flow Designer or IntegrationHub.
- Example: Data Cleanup Agent, Asset Auditor Agent.
Spoke-Based Agents
- Reusable logic packages used across modules.
- Example: Notification Agent Spoke that pushes messages to multiple channels.

Each category has distinct governance rules within the AI Control Tower.

🧩 Architecture Overview

+--------------------------------------------------------------+
|                     AI CONTROL TOWER                         |
|   Governance | Compliance | Monitoring | Lifecycle Mgmt       |
+--------------------------+-----------------------------------+
                           |
                           ▼
                 +-----------------------+
                 |  AI AGENT ORCHESTRATOR|
                 +-----------------------+
                     /          \
        +----------------+   +----------------+
        | Agent A (ITSM) |   | Agent B (HRSD) |
        +----------------+   +----------------+
             |                    |
             ▼                    ▼
    ServiceNow Workflows     External APIs / Data

This architecture illustrates the collaboration of multiple AI Agents under the orchestration of a single Control Tower — enabling enterprise-grade automation and compliance.

🔐 Security and Governance

ServiceNow ensures secure and compliant AI operations:

Scoped Access: Each agent operates under defined data scopes and roles.
Audit Logs: Every action is recorded for transparency.
Ethical AI: Bias detection and fairness validation features.
Regulatory Compliance: Alignment with GDPR, ISO 27001, SOC 2.

The Control Tower centralises oversight, allowing security teams to monitor and intervene when necessary.

🧭 Best Practices

✅ Start Small: Begin with a single, simple workflow (e.g., ticket classification).
✅ Iterate Quickly: Deploy, test, and refine using agile cycles.
✅ Use Templates: ServiceNow provides pre-built agent templates for common functions.
✅ Data Readiness: Ensure data quality and schema consistency before automation.
✅ Governance First: Always deploy under the Control Tower — never unmanaged.
✅ Cross-Functional Teams: Combine IT, data science, and compliance stakeholders.
✅ Measure ROI: Track automation rates, time saved, and MTTR reduction.

🧮 Metrics for Success

Metric	Definition	Target
Automation Rate	% of tasks completed autonomously	> 70%
MTTR Reduction	Mean time to resolution decrease	30–50%
User Satisfaction	End-user satisfaction post-automation	+20 points
Compliance Violations	Number of policy breaches	0
Operational Cost Savings	Year-over-year reduction	15–25%

🧰 Troubleshooting Tips

Agent not triggering: Verify use-case activation flag.
Agent fails mid-workflow: Check Orchestrator logs for missing permissions.
Slow execution: Optimise RAG queries and limit external API calls.
Unintended actions: Review decision tables and scopes in Control Tower.
Feedback loop not learning: Ensure analytics integration is enabled.

🧾 Summary

Agentic AI in ServiceNow transforms automation from static workflows into dynamic, intelligent operations.
By leveraging AI Agent Studio, AI Control Tower, and Orchestrator, organisations can:

Automate complex, cross-functional workflows
Maintain control through governance and compliance
Scale digital transformation across IT, HR, CSM, and Operations

Agentic AI is more than a feature — it’s a foundation for the next generation of autonomous enterprises.

🔗 References & Resources

ServiceNow Agentic AI – Official Overview
AI Agent Studio Documentation – docs.servicenow.com
ServiceNow Community – Agentic AI Discussions
[AI Control Tower Best Practices Whitepaper – 2025 Edition]
Workshop Series: Building AI Agents with Agent Studio (RSVP)
[LinkedIn Live: Agentic AI in Action – From Copilot to Autonomous Agent (2025)]