AI Support Agent Resolution Quality Playbook
Assesses completeness and effectiveness of AI-driven support interactions. Detects unresolved issues, improper escalation handling, and policy deviations to maintain service quality.
Start building
Deploy this kit stack into your workspace. Customize bricks, scoring, and outputs to match your team.
Without this playbook
Most teams handle ai support agent resolution quality through scattered call reviews, manager opinion, and isolated examples. Without a shared operational definition, the signals stay inconsistent and difficult to act on across volume.
With this playbook
A shared, repeatable lens for ai support agent resolution quality - with structured outputs you can route into coaching, reporting, and workflow automation. Every conversation produces evidence, not just opinions.
Built for
AI product managers, ML engineers, and trust & safety teams
When teams use it
- Model evaluation and release gates
- Governance review and policy enforcement
- Safety and accuracy monitoring
The operational stack
2 kits behind this playbook
An AI support agent can sound helpful while leaving the customer's issue unresolved. This stack measures three things that determine actual service quality: whether the AI provided a complete and correct resolution, whether it escalated appropriately when it should have and did not escalate unnecessarily when it should not have, and whether its responses stayed within support policy and knowledge base accuracy. The distinction matters - a correct answer delivered outside policy is a different problem than an incorrect answer delivered perfectly.
AI Resolution Completeness Kit
3 bricks
Measures whether AI provides full correct answers.
Included bricks
Customize this kitCompleteness Present
BooleanDetects if AI responses fully address customer queries
Response Quality Score
ScoreScores thoroughness and correctness of AI responses
Escalation Trigger Present
BooleanDetects whether AI incorrectly escalates or fails to escalate
Escalation Handling Kit
3 bricks
Detects appropriate escalation handling in AI support.
Included bricks
Customize this kitEscalation Trigger Present
BooleanDetects whether AI incorrectly escalates or fails to escalate
Resolution Action Present
BooleanDetects whether escalation was addressed with appropriate resolution language
Escalation Quality Score
ScoreScores how well escalation was handled
Knowledge base
Supporting materials
The kits in this playbook work best when backed by reference materials that ground the evaluation. Upload these into your workspace knowledge base to improve accuracy and relevance.
Learn more about Knowledge BasesSupport knowledge base and approved resolution documentation
Escalation criteria and transfer procedures
Support policy documentation and response guidelines
Quality scoring rubrics for support interactions
Known edge cases and failure patterns for AI support agents
Structured output
What you get back
Every conversation processed through this stack produces a structured JSON object. Each brick contributes a typed field - booleans, scores, categories, or string lists - that you can route, aggregate, and report on.
Example output shape
{
"completeness_present": true,
"response_quality_score": 7,
"escalation_trigger_present": true,
"resolution_action_present": true,
"escalation_quality_score": 7
}In practice
How teams use these outputs
The structured outputs from this stack integrate into your existing workflows. Use them wherever you need repeatable, evidence-based signal from conversations.
Model evaluation and release gates
Governance review and policy enforcement
Safety and accuracy monitoring
AI agent performance benchmarking
Get started
Deploy this playbook in your workspace
Customizing creates a workspace-owned draft with this playbook's full kit stack. Adjust bricks, scoring, and outputs to fit your team, then publish when ready.