Frootai Frootai Tune Voice AI Agent

Tune Voice AI Agent

When to Use

• Optimize dialog state flow (reduce unnecessary turns)
• Tune voice prosody for brand consistency
• Configure intent confidence thresholds
• Reduce response latency
• Manage conversation context window efficiently

Tuning Dimensions

Dimension 1: Dialog Flow Optimization

Dimension 2: Voice Prosody Tuning

Dimension 3: Intent Confidence Thresholds

Dimension 4: Context Window Management

Rule: Use sliding window (5 turns) by default. Switch to summary for conversations > 10 turns.

Dimension 5: Cost Per Conversation

Optimization: Use gpt-4o-mini for intent classification + gpt-4o for response = 40% LLM cost reduction.

Production Readiness Checklist

• Intent accuracy ≥ 92% on test utterances
• Dialog completion rate ≥ 80%
• Response latency < 2s end-to-end
• Voice MOS ≥ 4.0
• Multi-turn context retained across turns
• Escalation path working
• Proactive actions triggering correctly
• Conversation context managed (no overflow)
• Cost per conversation within budget

Output: Tuning Report

After tuning, compare:

• Dialog turn count reduction
• Intent accuracy improvement
• Latency reduction
• Voice quality (MOS) change
• Cost per conversation reduction

Tuning Playbook

1. Baseline: Run 20 dialog scenarios, record all metrics
2. Dialog: Merge greeting+intent, add implicit confirmations
3. Intent: Calibrate thresholds (>0.95=execute, 0.8-0.95=confirm, <0.8=clarify)
4. Voice: Test 3 voice styles, select by MOS score
5. Latency: Profile STT/LLM/TTS breakdown, optimize bottleneck
6. Context: Set sliding window to 5 turns, test retention
7. Cost: Route intents to gpt-4o-mini, responses to gpt-4o
8. Proactive: Test scheduled triggers, verify timing accuracy
9. Re-test: Same 20 scenarios, compare before/after

Play 04 vs Play 33 Decision Guide

Source: frootai/frootai — distributed by TomeVault.