Playbook Recommendations

Overview

This skill recommends relevant Process Automation Designer playbooks based on the context of an active case, incident, or HR case:

• Matching issue patterns (category, priority, symptoms) to existing playbook triggers and conditions
• Scoring playbook relevance based on historical success rates and contextual alignment
• Recommending specific playbooks with explanation of why they match
• Suggesting playbook customizations when no exact match exists
• Identifying gaps where new playbooks should be created
• Analyzing playbook execution history for effectiveness validation

When to use: When agents need guidance on which playbook to apply to an active record, when automating playbook selection in virtual agent flows, or when assessing playbook coverage across service categories.

Prerequisites

• Roles: process_automation_user, itil, sn_customerservice_agent, or admin
• Plugins: com.glide.process_automation (Process Automation Designer)
• Access: Read access to sys_pd_playbook, sys_pd_activity, sys_pd_context, and source record tables
• Knowledge: Process Automation Designer concepts, playbook lifecycle, activity types
• Related Skills: genai/playbook-generation for creating new playbooks, genai/flow-generation for underlying flows

Procedure

Step 1: Retrieve the Source Record Context

Fetch the case or incident that needs a playbook recommendation.

MCP Approach:

Tool: SN-Get-Record
Parameters:
  table_name: incident
  sys_id: <incident_sys_id>
  fields: sys_id,number,short_description,description,category,subcategory,priority,impact,urgency,assignment_group,state,cmdb_ci,contact_type,caller_id

REST Approach:

GET /api/now/table/incident/<incident_sys_id>
  ?sysparm_fields=sys_id,number,short_description,description,category,subcategory,priority,impact,urgency,assignment_group,state,cmdb_ci,contact_type
  &sysparm_display_value=true

For CSM cases:

Tool: SN-Get-Record
Parameters:
  table_name: sn_customerservice_case
  sys_id: <case_sys_id>
  fields: sys_id,number,short_description,description,product,category,priority,account,contact,state,assignment_group

Step 2: Query Available Playbooks

Retrieve all active playbooks that could apply to this record type.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: sys_pd_playbook
  query: active=true^trigger_table=incident^ORDERBYorder
  fields: sys_id,name,description,trigger_table,trigger_condition,category,sys_updated_on,active,application
  limit: 50

REST Approach:

GET /api/now/table/sys_pd_playbook
  ?sysparm_query=active=true^trigger_table=incident^ORDERBYorder
  &sysparm_fields=sys_id,name,description,trigger_table,trigger_condition,category,sys_updated_on
  &sysparm_display_value=true
  &sysparm_limit=50

Step 3: Retrieve Playbook Activities and Stages

For each candidate playbook, understand what it does by fetching its stages and activities.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: sys_pd_stage
  query: playbook=<playbook_sys_id>^ORDERBYorder
  fields: sys_id,name,description,order,playbook,condition
  limit: 20

Tool: SN-Query-Table
Parameters:
  table_name: sys_pd_activity
  query: playbook=<playbook_sys_id>^ORDERBYorder
  fields: sys_id,name,description,activity_type,stage,order,condition,mandatory,inputs
  limit: 50

Step 4: Match Playbooks to Record Context

Score each playbook against the source record using these criteria:

Matching Criteria:

Criterion	Weight	How to Match
Category match	30%	Playbook trigger_condition includes record's category
Priority alignment	15%	Playbook designed for this priority level
CI/Service match	20%	Playbook targets the same CI type or service
Description similarity	15%	NL similarity between record description and playbook description
Historical success	20%	Playbook resolution rate for similar records

MCP Approach for historical matching:

Tool: SN-Query-Table
Parameters:
  table_name: sys_pd_context
  query: playbook=<playbook_sys_id>^state=complete^ORDERBYDESCsys_created_on
  fields: sys_id,playbook,document_id,state,started,completed,duration
  limit: 50

Step 5: Calculate Relevance Scores

Build a ranked list of playbook recommendations:

=== PLAYBOOK RECOMMENDATIONS ===
Record: INC0045678 - Email server not responding
Category: Email | Priority: P2 | CI: mail-server-prod-01

Rank | Playbook                        | Score | Match Reason
-----|---------------------------------|-------|------------------------------------------
#1   | Email Service Outage Response   | 92%   | Category: exact, CI type: mail server,
     |                                 |       | Success rate: 89% (45 past executions)
#2   | Server Connectivity Diagnostic  | 78%   | CI type: server, Description: "not
     |                                 |       | responding" pattern, Success: 76%
#3   | Network Service Restoration     | 65%   | Category: partial (network/email),
     |                                 |       | Priority: P2 match, Success: 82%
#4   | General Incident Triage         | 45%   | Generic fallback, always applicable,
     |                                 |       | Success: 71%

RECOMMENDED: #1 - Email Service Outage Response
Reason: Exact category match, designed for this CI type, 89% historical
success rate across 45 similar incidents.

Step 6: Analyze Playbook Execution History

Check how the recommended playbook has performed historically.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: sys_pd_context
  query: playbook=<recommended_playbook_sys_id>^state=complete^completed>javascript:gs.daysAgo(90)
  fields: sys_id,document_id,state,started,completed,duration
  limit: 100

Calculate metrics:

=== PLAYBOOK PERFORMANCE: Email Service Outage Response ===
Period: Last 90 days

Executions: 45
Completion Rate: 89% (40 completed, 5 abandoned)
Average Duration: 32 minutes
Median Duration: 25 minutes
Fastest Resolution: 8 minutes
Slowest Resolution: 2 hours 15 minutes

Resolution Outcome (from linked incidents):
- Resolved: 38 (84%)
- Escalated: 5 (11%)
- Workaround Applied: 2 (5%)

Step 7: Suggest Playbook Customizations

When no playbook is an exact match, recommend modifications to the closest match:

=== CUSTOMIZATION SUGGESTIONS ===
Closest Match: Server Connectivity Diagnostic (78% relevance)

Suggested Modifications for Email-Specific Use:
1. ADD STAGE: "Check Email Service Status"
   - Activity: Query monitoring system for mail server health
   - Activity: Check mail queue depth via REST API

2. MODIFY STAGE: "Connectivity Tests"
   - Add email-specific port checks (25, 587, 993, 143)
   - Add SMTP handshake test activity

3. ADD ACTIVITY: "Notify Email Users"
   - Send communication to affected distribution list
   - Post status update to service status page

4. SKIP STAGE: "Database Connectivity" (not applicable to email)

Estimated Effort to Customize: 2-4 hours
Alternative: Create new playbook using genai/playbook-generation skill

Step 8: Identify Playbook Coverage Gaps

Find categories or incident types with no matching playbooks.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: active=true^sys_pd_contextISEMPTY^ORDERBYDESCsys_created_on
  fields: category,subcategory,priority,short_description
  limit: 100

Group by category to find unserved areas:

=== PLAYBOOK COVERAGE GAPS ===

| Category/Subcategory        | Open Incidents | Playbook Available |
|-----------------------------|---------------|-------------------|
| Hardware > Monitor Issues   | 23            | No                |
| Software > License Errors   | 18            | No                |
| Network > WiFi Connectivity | 15            | No                |
| Email > Calendar Sync       | 12            | No                |
| Database > Performance      | 8             | Yes (partial)     |

Priority Recommendation:
Create playbooks for "Hardware > Monitor Issues" and "Software > License Errors"
first (highest volume with no coverage).

Step 9: Attach Playbook to Record (Optional)

If the recommendation is accepted, associate the playbook with the record.

MCP Approach:

Tool: SN-Update-Record
Parameters:
  table_name: incident
  sys_id: <incident_sys_id>
  data:
    u_recommended_playbook: "<playbook_sys_id>"
    u_playbook_relevance_score: "92"

Step 10: Track Recommendation Effectiveness

Monitor whether recommended playbooks lead to successful outcomes.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: sys_pd_context
  query: document_id=<incident_sys_id>^state=complete
  fields: sys_id,playbook,state,started,completed,duration
  limit: 5

Tool Usage

Tool	Purpose	When to Use
SN-Query-Table	Fetch playbooks, execution history, incidents	Primary data retrieval and analysis
SN-Get-Record	Retrieve specific incident or case details	Getting source record context
SN-Natural-Language-Search	Find playbooks by description similarity	When category matching is insufficient
SN-Update-Record	Link recommended playbook to record	Recording the recommendation

Best Practices

1. Weight historical success heavily -- a playbook with 90% completion rate is better than a perfect category match with poor execution
2. Consider playbook complexity -- recommend simpler playbooks for P1 incidents where speed matters
3. Check playbook currency -- playbooks not updated in 6+ months may reference outdated procedures
4. Factor in agent skill level -- some playbooks require advanced technical skills
5. Provide fallback recommendations -- always include a generic triage playbook as a safety net
6. Track recommendation acceptance -- measure how often agents follow recommendations
7. Update scoring weights -- tune match criteria based on actual outcome data
8. Consider time-of-day -- some playbooks require resources only available during business hours
9. Avoid playbook overload -- recommend 3-5 options maximum, ranked by relevance
10. Include explanation -- agents need to understand why a playbook is recommended

Troubleshooting

Issue	Cause	Resolution
No playbooks found	Wrong trigger_table or all inactive	Check sys_pd_playbook.trigger_table matches record type
All scores are low	Playbooks not aligned with current categories	Review and update playbook trigger conditions
Execution history empty	Playbooks recently created or never used	Fall back to category/description matching only
Recommendation mismatch	Scoring weights not calibrated	Analyze past recommendations vs outcomes, adjust weights
Context records missing	Process Automation tracking not enabled	Verify sys_pd_context records are being created
Playbook activities not returned	Activities use different table name	Check for sys_pd_lane_activity or version-specific table

Examples

Example 1: Incident Playbook Recommendation

Input: "Recommend a playbook for INC0045678 - Email server not responding"

Steps: Retrieve incident details, query all active playbooks for incident table, score each against category/priority/CI/description, fetch execution history for top matches, return ranked list with explanations.

Example 2: CSM Case Playbook Matching

Input: "What playbook should I use for this customer complaint about billing errors?"

Steps: Retrieve case details from sn_customerservice_case, query playbooks with trigger_table=sn_customerservice_case, match on product/category/description, recommend with customer satisfaction metrics from past executions.

Example 3: Playbook Coverage Assessment

Input: "Show me which incident categories have no playbook coverage"

Steps: Query all active playbook trigger conditions, query all incident categories with volume counts, cross-reference to identify categories with incidents but no matching playbooks, prioritize gaps by incident volume.

Related Skills

• genai/playbook-generation - Create new playbooks for identified gaps
• genai/flow-generation - Build underlying flows for playbook activities
• itsm/incident-triage - Incident categorization and routing
• itsm/incident-lifecycle - Incident management process
• csm/case-summarization - Customer case context for matching