Automated Quality Assessment for Voice Agents
Automatic LLM Evaluation
Manual evaluation cannot scale.
Assessment
Turn-Level
Semantic + Syntactic
Conversation-Level
LLM Judge
Subjective scoring
Executive Summary
What we built
Automatic LLM Evaluation uses AI judges and user simulation to evaluate agent performance at scale — measuring relevance, informativeness, tone, task success, and policy adherence.
Why it matters
Manual evaluation cannot scale. Automated LLM evaluation enables consistent quality assessment across thousands of conversations, supporting CI/CD gates and continuous improvement.
Results
- ≥0.8 correlation with human raters when properly calibrated
- Turn-level and conversation-level assessment
- Reference-based and scenario-based evaluation
- Scalable to 50-100+ evaluations per change
Best for
- →Automated quality assessment at scale
- →CI/CD quality gates
- →Regression testing
- →A/B comparison of agent versions
Limitations
- Requires clear rubrics for reliable scoring
- Calibration needed for each evaluation dimension
- Reference-based evaluation requires human conversation data
How It Works
A two-layer detection system where each covers the other's weaknesses.
User Simulation
Automated conversation generation
- Scenario-based: LLM follows specific behaviors
- Reference-based: Mimic actual human conversations
- Edge case and adversarial testing
Turn-Level Evaluation
Per-response quality assessment
- Semantic: relevance, informativeness
- Syntactic: grammar, fluency
- LLM Judge: tone, style
Conversation-Level Evaluation
Overall interaction quality
- Task success: Did agent accomplish goal?
- Satisfaction: Would user be satisfied?
- Policy adherence: Did agent follow guidelines?
Product Features
Ready for production with enterprise-grade reliability.
Automated User Simulation
LLM-based scenario following for repeatable, scalable conversation generation.
Multi-Level Assessment
Both turn-level (relevance, tone) and conversation-level (task success, satisfaction) metrics.
Reference-Based Comparison
Compare AI behavior to actual human conversation transcripts.
Semantic & Syntactic Metrics
Cosine similarity, intent accuracy, grammar correctness, response length.
LLM Judge Scoring
Subjective evaluation via prompted LLM with calibrated rubrics.
CI/CD Integration
Quality gates for min_task_success, min_satisfaction, max_error_rate.
Integration Details
Runs On
Cloud (LLM Judge) + Bot-to-Bot Simulation
Latency Budget
Batch processing for evaluation
Providers
Bot-to-Bot Simulation, AVM Framework
Implementation
1-2 weeks for full pipeline setup
Frequently Asked Questions
Common questions about our voicemail detection system.
Ready to see this in action?
Book a technical walkthrough with our team to see how this research applies to your use case.