While the LLM as a Judge evaluation relies solely on a language model to assess responses, Agent as a Judge extends this concept by incorporating additional reasoning capabilities, decision-making processes, and external validation mechanisms. Instead of using a standalone LLM for evaluation, an agent orchestrates multiple steps to refine and validate judgments before producing a final evaluation score.

This approach enhances evaluation robustness, particularly for complex AI-generated outputs requiring multiple checks or nuanced assessments beyond a single-pass LLM evaluation.

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Working of Agent-Based Evaluation

  1. Agent Initialisation:
    • The agent is initialised with specific model configurations, temperature settings, and optional capabilities like internet verification.
    • This setup enables the agent to adapt to different evaluation scenarios and potentially access real-time data for validation.
  2. Evaluation Process:
    • Planning Phase: The agent begins by generating a structured evaluation plan. This plan outlines the steps it will take to assess the content, ensuring a comprehensive evaluation approach.
    • Execution Phase: The agent executes the evaluation based on the plan, using Chain of Thought (CoT) reasoning to break down complex tasks into simpler sub-tasks. This approach enables the agent to tackle nuanced evaluation criteria, adapt its assessments, and provide more insightful feedback.
    • External Validation: If internet access is enabled, the agent can verify facts or gather additional context, which is particularly useful for ensuring accuracy and relevance in responses.
  3. Scoring System:
    • The agent uses a 0-10 scoring scale, providing granular feedback on the quality and relevance of the content:
      • 10: Fully relevant, comprehensive response.
      • 8: Good response with minor omissions.
      • 6: Average response with some inaccuracies.
      • 4: Partial response with significant gaps.
      • 0: Incomplete or incorrect response.
    • This scoring system allows for a detailed analysis of the content, identifying strengths and areas for improvement.

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