Build Custom AI Agents

Overview

This skill provides a comprehensive guide to building custom AI agents in ServiceNow:

• Defining agent capabilities and scope of responsibility
• Configuring knowledge sources for agent grounding (KB articles, catalog items, documentation)
• Setting up tool access so agents can perform actions (create records, query data, trigger flows)
• Designing conversational flows with intent recognition and entity extraction
• Implementing guardrails for safety, accuracy, and compliance
• Testing and iterating on agent behavior before production deployment

When to use: When building a new virtual agent topic, extending Now Assist skill capabilities, creating a domain-specific AI assistant, or configuring agent tool access for automated task execution.

Prerequisites

• Roles: admin, va_admin, or now_assist_admin
• Plugins: com.glide.cs.chatbot (Virtual Agent), com.snc.now_assist (Now Assist) recommended
• Access: Write access to sys_cs_topic, sys_cs_intent, sys_cs_entity, sys_cb_topic, kb_knowledge tables
• Knowledge: Understanding of conversational AI concepts (intents, entities, slots, fulfillment)
• Related Skills: genai/agent-miner for discovering what to automate, genai/skill-kit-custom for Now Assist skills

Procedure

Step 1: Define Agent Scope and Capabilities

Document the agent's purpose, target users, and boundaries.

=== AGENT DEFINITION ===
Name: [Agent Name]
Domain: [IT Support / HR / Facilities / Custom]
Target Users: [Employee / Customer / Technician]

CAPABILITIES (What the agent CAN do):
- [ ] Answer questions about [topic]
- [ ] Create [record type] on behalf of users
- [ ] Look up [information type]
- [ ] Trigger [workflow/process]
- [ ] Escalate to [team] when needed

BOUNDARIES (What the agent CANNOT do):
- [ ] Access [restricted data/system]
- [ ] Approve [request type]
- [ ] Modify [critical records]
- [ ] Share [sensitive information]

SUCCESS METRICS:
- Deflection Rate Target: [%]
- User Satisfaction Target: [score]
- Resolution Rate Target: [%]
- Average Handle Time Target: [minutes]

Step 2: Configure Knowledge Sources

Set up the knowledge base articles and data sources the agent will use for grounding.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: kb_knowledge
  query: kb_knowledge_base=[KB_SYS_ID]^workflow_state=published^active=true
  fields: sys_id,number,short_description,kb_category,article_type
  limit: 100

REST Approach:

GET /api/now/table/kb_knowledge
  ?sysparm_query=kb_knowledge_base=[KB_SYS_ID]^workflow_state=published^active=true
  &sysparm_fields=sys_id,number,short_description,kb_category,article_type
  &sysparm_limit=100

Verify knowledge coverage for each agent capability:

Capability	KB Articles	Gaps Identified
Password reset	KB001, KB002	None
VPN setup	KB010	Missing macOS instructions
Software request	None	Need catalog mapping article

Step 3: Create Agent Topic

Build the primary topic that defines the agent's conversational scope.

MCP Approach:

Tool: SN-Create-Record
Parameters:
  table_name: sys_cs_topic
  data:
    name: "IT Support Assistant"
    description: "Handles common IT support requests including password resets, VPN access, and software installations"
    state: draft
    category: IT
    greeting_message: "Hi! I'm your IT Support Assistant. I can help with password resets, VPN access, software requests, and general IT questions. What do you need help with?"
    no_match_message: "I'm not sure I understand. Could you rephrase your request, or would you like me to connect you with a live agent?"
    max_retries: 3

REST Approach:

POST /api/now/table/sys_cs_topic
Body: {
  "name": "IT Support Assistant",
  "description": "Handles common IT support requests",
  "state": "draft",
  "category": "IT",
  "greeting_message": "Hi! I'm your IT Support Assistant...",
  "no_match_message": "I'm not sure I understand...",
  "max_retries": "3"
}

Step 4: Define Intents and Training Utterances

Create intents that the agent should recognize and respond to.

MCP Approach:

Tool: SN-Create-Record
Parameters:
  table_name: sys_cs_intent
  data:
    name: "Reset Password"
    topic: [TOPIC_SYS_ID]
    description: "User wants to reset their password for any system"
    training_utterances: |
      I need to reset my password
      My password expired
      I can't log in
      Password not working
      Forgot my password
      Need a new password
      Locked out of my account
      Account locked
      Can't access my account
      Reset credentials

Repeat for each capability:

Intent	Sample Utterances	Entity Slots
Reset Password	"reset my password", "locked out"	system_name, username
VPN Access	"connect to VPN", "VPN not working"	vpn_type, device_type
Software Install	"install software", "need [app]"	software_name, justification
General Question	"how do I...", "what is..."	topic_keyword

Step 5: Define Entities for Slot Filling

Create entities to extract structured data from user messages.

MCP Approach:

Tool: SN-Create-Record
Parameters:
  table_name: sys_cs_entity
  data:
    name: "system_name"
    topic: [TOPIC_SYS_ID]
    type: "list"
    prompt: "Which system do you need the password reset for?"
    required: true
    values: |
      Active Directory|AD|Windows login
      Email|Outlook|Exchange
      VPN|Cisco AnyConnect
      SAP|ERP
      Salesforce|CRM

REST Approach:

POST /api/now/table/sys_cs_entity
Body: {
  "name": "system_name",
  "topic": "[TOPIC_SYS_ID]",
  "type": "list",
  "prompt": "Which system do you need the password reset for?",
  "required": "true"
}

Step 6: Configure Tool Access and Actions

Define what actions the agent can perform in ServiceNow.

MCP Approach:

Tool: SN-Create-Record
Parameters:
  table_name: sys_cb_action
  data:
    topic: [TOPIC_SYS_ID]
    name: "Create Incident for Password Reset"
    action_type: "create_record"
    table: "incident"
    field_values: |
      category=inquiry
      subcategory=password
      short_description=Password reset request: ${system_name}
      assignment_group=IT Help Desk
      caller_id=${sys_user}
      urgency=3
      impact=3

REST Approach:

POST /api/now/table/sys_cb_action
Body: {
  "topic": "[TOPIC_SYS_ID]",
  "name": "Create Incident for Password Reset",
  "action_type": "create_record",
  "table": "incident",
  "field_values": "category=inquiry^subcategory=password^short_description=Password reset request"
}

Common Action Types:

Action Type	Purpose	Configuration
create_record	Create a new record	table, field_values
update_record	Update existing record	table, sys_id, field_values
query_record	Look up records	table, query, return_fields
run_flow	Trigger a Flow Designer flow	flow_sys_id, inputs
run_script	Execute server-side script	script body
kb_search	Search knowledge base	query, kb_base
escalate	Transfer to live agent	queue, message

Step 7: Implement Guardrails

Configure safety boundaries and compliance controls.

Input Guardrails:

Tool: SN-Create-Record
Parameters:
  table_name: sys_cs_topic_goal
  data:
    topic: [TOPIC_SYS_ID]
    name: "Input Validation"
    type: "guardrail"
    configuration: |
      BLOCKED_INTENTS:
      - Requests for other users' personal data
      - Attempts to bypass approval processes
      - Requests for admin/root access

      INPUT_SANITIZATION:
      - Strip HTML/script tags from user input
      - Validate employee ID format before lookup
      - Limit free-text input to 500 characters

      AUTHENTICATION:
      - Require verified session for record creation
      - Verify caller identity before sharing account details
      - MFA challenge for sensitive operations

Output Guardrails:

RESPONSE RULES:
- Never expose sys_id values or internal table names to users
- Never share other employees' personal information
- Always confirm destructive actions before executing
- Include disclaimer for estimated timeframes
- Redirect compliance/legal questions to appropriate teams
- Log all agent actions for audit trail

ESCALATION TRIGGERS:
- User expresses frustration (sentiment < -0.5)
- 3 consecutive failed intent matches
- Request involves VIP or executive user
- Security-related request (account compromise)
- Agent confidence score below threshold (< 0.7)

Step 8: Design Conversation Flows

Map out the full conversational flow for each intent.

CONVERSATION FLOW: Password Reset

[User Message] -> Intent Recognition -> "Reset Password" (confidence > 0.8)
  |
  v
[Slot: system_name] -> "Which system?" -> User provides system
  |
  v
[Slot: username] -> "What is your username?" -> User provides username
  |
  v
[Validation] -> Verify user exists in system
  |-- User found -> Proceed
  |-- User not found -> "I couldn't find that username. Please verify and try again."
  |
  v
[Action] -> Trigger password reset flow
  |-- Success -> "Your password has been reset. Check your email for the temporary password."
  |-- Failure -> "I wasn't able to reset your password. Let me connect you with a live agent."
  |
  v
[Follow-up] -> "Is there anything else I can help you with?"

Step 9: Test the Agent

Validate agent behavior with test scenarios.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: sys_cs_topic
  query: sys_id=[TOPIC_SYS_ID]
  fields: sys_id,name,state,test_status,last_tested

Test Matrix:

Test Case	Input	Expected Behavior	Result
Happy path	"Reset my AD password"	Collect username, trigger reset
Missing entity	"Reset my password"	Ask which system
Unknown intent	"What's the weather?"	No-match response
Guardrail trigger	"Reset John's password"	Refuse, explain why
Escalation	"This isn't working, get me a human"	Transfer to live agent
Edge case	Empty message	Prompt for input

Step 10: Deploy and Monitor

Publish the agent and set up monitoring.

MCP Approach:

Tool: SN-Update-Record
Parameters:
  table_name: sys_cs_topic
  sys_id: [TOPIC_SYS_ID]
  data:
    state: published

Post-Deployment Monitoring Queries:

Tool: SN-Query-Table
Parameters:
  table_name: sys_cs_conversation
  query: topic=[TOPIC_SYS_ID]^sys_created_on>=javascript:gs.daysAgo(7)
  fields: sys_id,state,live_agent,transfer_reason,user_satisfaction,sys_created_on
  limit: 100

Tool Usage

Tool	Purpose	When to Use
SN-Query-Table	Find existing topics, intents, and knowledge articles	Discovery and validation
SN-Create-Record	Create topics, intents, entities, and actions	Building agent components
SN-Update-Record	Modify agent settings, publish topics	Configuration and deployment
SN-Get-Table-Schema	Discover topic and intent table structures	Initial setup
SN-Get-Record	Get detailed record for specific topic or intent	Reviewing existing configurations

Best Practices

1. Start narrow, expand gradually -- launch with 2-3 well-defined intents before adding more
2. Use at least 10 training utterances per intent for reliable recognition
3. Test with real user language -- avoid technical jargon in training data
4. Implement graceful fallbacks -- always provide a path to live agent assistance
5. Monitor and retrain weekly during the first month after deployment
6. Version your topics -- maintain draft versions while published version serves users
7. Separate concerns -- one topic per domain area (IT, HR, Facilities)
8. Log all actions -- maintain audit trail for compliance and debugging
9. Set confidence thresholds -- do not act on low-confidence intent matches
10. Gather user feedback -- use post-conversation surveys to identify improvement areas

Troubleshooting

Issue	Cause	Resolution
Agent not recognizing intents	Insufficient or poor training utterances	Add more diverse utterances; include typos and abbreviations
Wrong intent matched	Overlapping utterances between intents	Review and deduplicate training data; increase confidence threshold
Entity not extracted	Entity values do not match user input	Add synonyms and variations to entity value list
Action fails silently	Missing permissions or invalid field mapping	Check agent user roles; validate field names against table schema
Agent loops on same question	Required slot not being filled by user response	Add skip logic or alternative slot-filling prompts
High escalation rate	Agent scope too narrow or responses not helpful	Expand knowledge sources; review escalated conversations for patterns

Examples

Example 1: IT Support Bot

Scenario: Build a virtual agent for common IT support requests.

AGENT: IT Support Assistant
INTENTS:
1. Password Reset -> Flow: Reset Password (AD, Email, VPN)
2. Software Request -> Action: Create RITM from catalog
3. VPN Troubleshooting -> KB Search: VPN articles
4. Hardware Issue -> Action: Create incident, escalate if P1
5. General IT Question -> KB Search: IT knowledge base

GUARDRAILS:
- Cannot reset passwords for other users
- Software requests require manager approval flow
- Hardware replacements require asset verification

Example 2: HR Benefits Bot

Scenario: Build an agent for benefits enrollment questions.

AGENT: Benefits Assistant
INTENTS:
1. Benefits Overview -> KB: Benefits summary article
2. Enrollment Period -> KB: Open enrollment dates and process
3. Plan Comparison -> KB: Plan comparison matrix
4. Life Event Change -> Action: Create HR case for life event
5. Dependent Add/Remove -> Action: Create HR case with dependent info

GUARDRAILS:
- Cannot share other employees' benefits information
- Directs medical questions to benefits provider
- Logs all interactions for compliance

Example 3: Facilities Service Bot

Scenario: Build an agent for workplace service requests.

AGENT: Facilities Assistant
INTENTS:
1. Room Booking -> Action: Query room availability, create reservation
2. Maintenance Request -> Action: Create facilities incident
3. Badge Access -> Action: Create access request RITM
4. Parking -> KB: Parking policy and waitlist info
5. Catering -> Action: Create catering request with details

GUARDRAILS:
- Room bookings limited to 4 hours maximum
- Maintenance requests auto-prioritized by type
- Badge access requires security team approval

Related Skills

• genai/agent-miner - Discover automation opportunities from interaction data
• genai/flow-generation - Build flows that agents can trigger
• genai/playbook-generation - Create playbooks for agent-assisted workflows
• genai/skill-kit-custom - Build custom Now Assist skills
• knowledge/article-generation - Generate KB articles for agent knowledge sources