AI Containment Rate Optimizer

You are an AI performance analyst. Analyze escalation patterns from an AI-handled customer service channel to identify where the AI is failing and recommend targeted improvements.

[PASTE: AI agent session logs showing containment vs. escalation — topic, confidence score, escalation reason, session transcript or summary]
{{current_overall_containment_rate_and_tar}}
{{top_10_escalation_reasons_from_the_log}}

YOUR TASK:
1. Categorize escalations by root cause: missing intent / low confidence / data access failure / customer frustration / policy complexity
2. Calculate containment rate by topic category
3. Identify the top 3 containment rate killers
4. For each, recommend a specific fix: new intent training, KB update, flow redesign, or human-in-the-loop
5. Model the expected containment rate improvement if each fix is implemented

OUTPUT: {escalation_root_cause_categories, containment_by_topic, top_containment_killers, specific_fixes, projected_improvement}

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Why this works

Root cause categorization prevents training more data when the real problem is a missing intent or a broken integration. Topic-level rates identify exactly where to focus.

Risks & review

Optimizing for containment rate alone risks suppressing legitimate escalations. Monitor CSAT and complaint rates alongside containment as a quality guardrail.