ConversationCoherenceScorer
Evaluates the logical flow and coherence of the agent's responses throughout the conversation. Assesses whether the agent maintains consistent reasoning and clear communication across all interactions.
Overview
Evaluates the logical flow and coherence of the agent's responses throughout the conversation. Assesses whether the agent maintains consistent reasoning and clear communication across all interactions.
Use Cases
- Autonomous AI agent evaluation
- Conversational AI quality assessment
How It Works
This scorer uses LLM-as-Judge technology to evaluate responses. It prompts a large language model with specific evaluation criteria and the content to assess, then analyzes the LLM's judgment to produce a score and detailed reasoning.
Input Schema
| Parameter | Type | Required | Description |
|---|---|---|---|
| agent_data.trace | list[dict] | str | Yes | Complete agent interaction history |
| agent_data.agent_exit | bool | Yes | Whether the agent has completed its task (must be True) |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Score (0-10 scale) |
| passed | bool | True if score meets threshold |
| reasoning | str | Detailed evaluation explanation |
| metadata.original_task | str | The extracted original task |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use ConversationCoherenceScorer when you need to evaluate agent and conversational aspects of your AI outputs. It's particularly useful for autonomous ai agent evaluation.
Why doesn't this scorer need expected output?
This scorer evaluates quality aspects that don't require comparison against a reference answer. It uses the system prompt and context as the implicit ground truth.
Can I customize the threshold?
Yes, the default threshold of 7 can be customized when configuring the scorer.
Quick Info
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