Conversation Evaluator

Overview

This skill evaluates virtual agent conversations across multiple quality dimensions to ensure accurate, coherent, and helpful interactions. It identifies issues with conversational AI performance and provides actionable improvement recommendations.

• Assess conversation coherence: logical flow, context retention, and topic continuity
• Validate accuracy: are responses factually correct and aligned with knowledge base content
• Evaluate slot filling: completeness and correctness of captured user inputs
• Score intent matching: did the virtual agent select the correct topic and actions
• Detect hallucination: identify fabricated information not grounded in ServiceNow data
• Generate quality scorecards with per-dimension ratings and aggregate scores

When to use: When auditing virtual agent performance, investigating user complaints about bot interactions, or running periodic quality assessments on conversational AI deployments.

Prerequisites

• Roles: admin, va_admin, or conversation_designer
• Plugins: com.glide.cs.chatbot (Virtual Agent), com.glide.cs.designer (Conversation Designer)
• Access: Read on sys_cs_topic, sys_cs_conversation, sys_cb_topic, sys_cb_action, sys_cs_message
• Knowledge: Understanding of your Virtual Agent topic designs and expected conversation flows
• Data: Active virtual agent deployment with conversation history

Procedure

Step 1: Retrieve Conversations for Evaluation

Query recent virtual agent conversations, optionally filtered by topic or outcome.

Using MCP (Claude Code/Desktop):

Tool: SN-Query-Table
Parameters:
  table_name: sys_cs_conversation
  query: state=closed^sys_created_onONLast 7 days@javascript:gs.daysAgoStart(7)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,number,topic,state,channel,opened_at,closed_at,close_reason,user,queue_time,handle_time
  limit: 50

Using REST API:

GET /api/now/table/sys_cs_conversation?sysparm_query=state=closed^sys_created_onONLast 7 days@javascript:gs.daysAgoStart(7)@javascript:gs.daysAgoEnd(0)&sysparm_fields=sys_id,number,topic,state,channel,opened_at,closed_at,close_reason,user,queue_time,handle_time&sysparm_limit=50

Step 2: Extract Conversation Messages

Retrieve the full message history for each conversation being evaluated.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sys_cs_message
  query: conversation=[conversation_sys_id]^ORDERBYsys_created_on
  fields: sys_id,conversation,body,type,sender_type,sys_created_on,is_hidden
  limit: 100

Using REST API:

GET /api/now/table/sys_cs_message?sysparm_query=conversation=[conversation_sys_id]^ORDERBYsys_created_on&sysparm_fields=sys_id,conversation,body,type,sender_type,sys_created_on,is_hidden&sysparm_limit=100

Step 3: Retrieve Topic and Action Definitions

Get the designed topic flow to compare against actual conversation behavior.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sys_cb_topic
  query: sys_id=[topic_sys_id]
  fields: sys_id,name,description,goal,utterances,active
  limit: 1

Using REST API:

GET /api/now/table/sys_cb_topic?sysparm_query=sys_id=[topic_sys_id]&sysparm_fields=sys_id,name,description,goal,utterances,active&sysparm_limit=1

Retrieve the expected actions for this topic:

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sys_cb_action
  query: topic=[topic_sys_id]^ORDERBYorder
  fields: sys_id,name,type,topic,order,script,table,field,mandatory
  limit: 50

Using REST API:

GET /api/now/table/sys_cb_action?sysparm_query=topic=[topic_sys_id]^ORDERBYorder&sysparm_fields=sys_id,name,type,topic,order,script,table,field,mandatory&sysparm_limit=50

Step 4: Evaluate Coherence (Score 0-10)

Assess the logical flow and context management of the conversation:

Criterion	Points	Description
Logical flow	0-3	Responses follow a logical sequence without non-sequiturs
Context retention	0-2	Bot remembers earlier user inputs throughout conversation
Topic continuity	0-2	Conversation stays on-topic without unexplained jumps
Greeting/closing	0-1	Appropriate opening and closing messages
Recovery from confusion	0-2	Bot handles misunderstandings gracefully

Red flags:

• Bot repeats the same question after user already answered
• Bot references information the user never provided
• Conversation jumps to unrelated topic without transition
• Bot fails to acknowledge user corrections

Step 5: Evaluate Accuracy (Score 0-10)

Verify responses against ServiceNow data sources:

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sys_cs_context_entry
  query: conversation=[conversation_sys_id]
  fields: sys_id,name,value,source,conversation
  limit: 50

Criterion	Points	Description
Factual correctness	0-3	Responses match ServiceNow records and KB articles
Data freshness	0-2	Information is current, not outdated
Source attribution	0-2	Bot references correct records/articles
No contradictions	0-2	Bot does not contradict itself within conversation
Appropriate uncertainty	0-1	Bot expresses uncertainty when data is ambiguous

Step 6: Evaluate Slot Filling (Score 0-10)

Check if required user inputs were correctly captured:

Criterion	Points	Description
Required slots captured	0-3	All mandatory fields collected from user
Slot validation	0-2	Inputs validated (e.g., email format, date range)
Slot confirmation	0-2	Bot confirms critical inputs back to user
Graceful re-prompting	0-2	Bot handles invalid inputs with clear guidance
Slot carryover	0-1	Previously provided info reused, not re-asked

Cross-reference captured slots with sys_cb_action definitions where mandatory=true.

Step 7: Evaluate Intent Matching (Score 0-10)

Determine if the virtual agent selected the correct topic for the user's request:

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sys_cs_topic
  query: active=true
  fields: sys_id,name,description,utterances
  limit: 100

Criterion	Points	Description
Correct topic selected	0-4	Initial intent classification matches user request
Disambiguation quality	0-2	When ambiguous, bot offers relevant topic choices
Topic switching	0-2	Handles mid-conversation topic changes appropriately
Fallback behavior	0-2	Graceful degradation when no topic matches

Step 8: Detect Hallucinations

Identify fabricated content not grounded in ServiceNow data:

Hallucination categories:

Type	Description	Severity
Fabricated record	Bot cites an incident/KB number that does not exist	Critical
Invented procedure	Bot describes steps not in any KB article or playbook	High
False status	Bot states incorrect ticket status or assignment	High
Made-up policy	Bot references a policy that does not exist	Medium
Incorrect attribution	Bot attributes information to wrong source	Medium

For each bot response containing specific claims, verify against the source table:

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: kb_knowledge
  query: number=[cited_kb_number]
  fields: sys_id,number,short_description,workflow_state
  limit: 1

Step 9: Generate Quality Scorecard

Compile evaluation results into a structured scorecard:

=== VIRTUAL AGENT CONVERSATION QUALITY SCORECARD ===
Conversation: CS0045678
Topic: Password Reset
Date: 2026-03-19
Evaluator: AI Quality Evaluator v1.0

Dimension Scores:
  Coherence:      8/10  - Minor context loss at turn 5
  Accuracy:       9/10  - All references verified correct
  Slot Filling:   7/10  - Did not confirm email address
  Intent Match:   10/10 - Correct topic on first utterance
  Hallucination:  10/10 - No fabricated content detected

Aggregate Score: 44/50 (88%) - GOOD

Issues Found:
  1. [MEDIUM] Turn 5: Bot asked for department after user already provided it
  2. [LOW] Turn 8: Bot did not confirm email address before sending reset link

Recommendations:
  1. Add context check for department slot before re-prompting
  2. Add confirmation step for email address in password reset topic

Step 10: Track Quality Trends

Aggregate scores across conversations to identify systemic issues:

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: sys_cs_conversation
  query: state=closed^close_reason=escalated^sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,topic,close_reason,handle_time
  limit: 200

Key quality metrics:

• Average score per topic: Identifies worst-performing conversation flows
• Escalation rate: Percentage of conversations escalated to human agents
• Hallucination frequency: How often fabricated content appears
• Slot completion rate: Percentage of required slots successfully captured

Tool Usage

Tool	Purpose	When to Use
SN-Query-Table	Retrieve conversations, messages, topics, actions	Core evaluation data gathering
SN-Natural-Language-Search	Find conversations by natural language criteria	Targeted investigation
SN-Update-Record	Flag conversations with quality issues	Marking for review
SN-Add-Work-Notes	Document evaluation findings	Audit trail
SN-Get-Table-Schema	Explore conversation-related table structures	Setup and discovery

Best Practices

1. Sample strategically -- evaluate across all topics, not just high-volume ones
2. Include escalated conversations -- escalations reveal the most significant quality gaps
3. Evaluate in batches -- assess 20-50 conversations per evaluation cycle for statistical validity
4. Separate dimensions -- a high coherence score does not compensate for hallucinations
5. Track over time -- single-point evaluations miss trends; run evaluations weekly
6. Involve topic designers -- share findings with the team that built the conversation flows
7. Weight severity -- hallucinations and accuracy issues should weigh more than minor coherence gaps
8. Benchmark against human agents -- compare VA scores with human agent CSAT for context

Troubleshooting

"No messages found for conversation"

Cause: Messages may be stored in a different table or the conversation was very short Solution: Check sys_cs_message and also sys_journal_field for related entries. Very short conversations (1-2 turns) may only have system messages.

"Topic sys_id does not match any sys_cb_topic"

Cause: The conversation used a legacy or custom topic framework Solution: Check sys_cs_topic in addition to sys_cb_topic. Some deployments use the newer Conversation Designer tables.

"Cannot determine if response is a hallucination"

Cause: The response is vague enough that it cannot be verified against specific records Solution: Flag as "unverifiable" rather than hallucination. Focus on responses with specific claims (ticket numbers, KB references, dates, names).

"Scores vary widely for the same topic"

Cause: Topic flow has conditional branches that behave differently based on user input Solution: Segment evaluations by conversation path (e.g., successful resolution vs. escalation path) for more meaningful comparisons.

Examples

Example 1: High-Quality Password Reset Conversation

Conversation: CS0045678 - User requests password reset

Evaluation:

• Coherence: 9/10 -- smooth flow, context maintained throughout
• Accuracy: 10/10 -- correctly referenced KB0012345 for self-service reset
• Slot Filling: 9/10 -- captured username, verified email, confirmed identity
• Intent Match: 10/10 -- matched "password reset" topic immediately
• Hallucination: 10/10 -- no fabricated content

Score: 48/50 (96%) - EXCELLENT

Example 2: Poor VPN Troubleshooting Conversation

Conversation: CS0045679 - User reports VPN issues

Evaluation:

• Coherence: 5/10 -- asked for OS twice, lost context of error message
• Accuracy: 4/10 -- referenced KB0099999 which does not exist (hallucination)
• Slot Filling: 6/10 -- missed mandatory field: VPN client version
• Intent Match: 7/10 -- initially matched "network issues" before correcting to VPN
• Hallucination: 2/10 -- fabricated KB article number and invented resolution steps

Score: 24/50 (48%) - POOR. Requires immediate topic redesign.

Example 3: Ambiguous Intent Handling

Conversation: CS0045680 - User says "it's broken"

Evaluation:

• Coherence: 8/10 -- bot asked clarifying questions methodically
• Accuracy: 9/10 -- once topic identified, responses were correct
• Slot Filling: 7/10 -- took 4 turns to capture enough detail
• Intent Match: 6/10 -- disambiguation menu was too broad (8 options)
• Hallucination: 10/10 -- no fabricated content

Score: 40/50 (80%) - GOOD. Recommendation: reduce disambiguation options to top 4.

Related Skills

• genai/chat-summarization-va - Summarize conversations after evaluation
• genai/now-assist-qa - Quality assurance for Now Assist features
• genai/playbook-generation - Build improved conversation flows
• itsm/incident-triage - Handle incidents escalated from virtual agent
• knowledge/content-recommendation - Verify KB article references