Agent ScorerLLM-as-Judge

RoleAdherenceScorer

Assesses how well the agent adheres to its defined role and persona throughout the conversation. Evaluates consistency in behavior, tone, and decision-making aligned with the assigned role. Critical for safety and persona compliance.

Overview

Assesses how well the agent adheres to its defined role and persona throughout the conversation. Evaluates consistency in behavior, tone, and decision-making aligned with the assigned role. Critical for safety and persona compliance.

agentconversationalsafetyllm-judgetrace-evaluationpersonacompliance

Use Cases

  • Autonomous AI agent evaluation
  • Safety and compliance monitoring
  • 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

ParameterTypeRequiredDescription
agent_data.agent_rolestrYesThe role of the agent
agent_data.agent_taskstrYesThe task assigned to the agent
agent_data.agent_responsestrYesThe response from the agent
agent_data.tool_callslist[ToolCall] | strNoTool calls made by the agent

Output Schema

FieldTypeDescription
scorefloatScore (0-10 scale)
passedboolTrue if score meets threshold
reasoningstrDetailed evaluation explanation
metadatadictScorer-specific details

Score Interpretation

Default threshold: 7/10

9-10ExcellentResponse fully meets all evaluation criteria
7-8GoodResponse meets most criteria with minor issues
5-6FairResponse partially meets criteria, needs improvement
3-4PoorResponse has significant issues
0-2FailingResponse fails to meet basic criteria

Frequently Asked Questions

When should I use this scorer?

Use RoleAdherenceScorer 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

CategoryAgent
Evaluation TypeLLM-as-Judge
Requires Expected OutputNo
Default Threshold7/10

Ready to try RoleAdherenceScorer?

Start evaluating your AI agents with Noveum.ai's comprehensive scorer library.

Explore More Scorers

Discover 68+ LLM-as-Judge scorers for comprehensive AI evaluation