Suggestions are actionable recommendations for improving your agent’s performance, generated by analyzing patterns in your data.Documentation Index
Fetch the complete documentation index at: https://docs.duckie.ai/llms.txt
Use this file to discover all available pages before exploring further.
How Suggestions Work
Duckie analyzes:- Common questions that lack good answers
- Frequently escalated scenarios
- Response patterns and quality
- Knowledge gaps and usage
Types of Suggestions
Knowledge Article Suggestions
New articles to create based on common questions:Suggested: Create article about “two-factor authentication setup” Reason: 45 conversations this week asked about 2FA with no matching knowledge. Impact: Could resolve ~40 conversations per week automatically.Action: Click to create a pre-filled article.
Runbook/Workflow Suggestions
Process improvements based on execution patterns:Suggested: Add step to verify email before password reset Reason: 30% of password reset escalations were due to unverified identity. Impact: Could reduce escalations by ~25%.
Guideline Suggestions
Communication improvements based on response analysis:Suggested: Add guideline to always confirm the specific issue before troubleshooting Reason: Many conversations required clarification after initial response.
Guardrail Suggestions
Safety improvements:Suggested: Add escalation rule for pricing negotiations Reason: 15 conversations about custom pricing were handled inconsistently.
Viewing Suggestions
Suggestion Details
Each suggestion includes:| Section | Content |
|---|---|
| Recommendation | What to do |
| Reason | Why it’s suggested |
| Evidence | Data supporting the suggestion |
| Impact | Expected improvement |
| Related conversations | Examples that drove this suggestion |
Acting on Suggestions
Accept
Implement the suggestion:Implement
Follow the guided flow to implement:
- Knowledge: Opens editor with pre-filled content
- Runbook: Opens editor with suggested changes
- Guideline: Opens settings with recommendation
Dismiss
Mark as not relevant:- Click Dismiss
- Select a reason (optional)
- Suggestion won’t reappear
Save for Later
Keep for future consideration:- Click Save for Later
- Suggestion moves to saved queue
- Review when ready
Suggestion Priority
Suggestions are ranked by:| Factor | Description |
|---|---|
| Impact | How much improvement expected |
| Confidence | How certain the analysis is |
| Frequency | How often the issue occurs |
| Effort | How much work to implement |
Suggestion Sources
Where suggestions come from:Knowledge Gaps
Questions the agent couldn’t answer well:- Reviewed in the Gaps tab
- Cross-referenced with conversation patterns
- Suggested when gap is significant
Escalation Analysis
Patterns in escalated conversations:- Common topics in escalations
- Missing knowledge that caused escalation
- Guardrail trigger patterns
Response Quality
Analysis of agent responses:- Conversations that needed clarification
- Low-rated responses
- Repeated customer follow-ups
Performance Data
Metrics that indicate issues:- Categories with low resolution
- Increasing escalation trends
- Response time outliers
Suggestion History
View past suggestions:- Implemented: Suggestions you accepted
- Dismissed: Suggestions you skipped
- Expired: Old suggestions no longer relevant
Next Steps
Knowledge Gaps
Fill knowledge gaps
Insights
View analytical insights