Agent Evaluation

What it does

The neolabhq-agent-evaluation skill provides a structured framework for evaluating the performance of AI agents. It allows users to define evaluation criteria, run agent executions against specific tasks, and then systematically assess results based on those criteria. This facilitates objective comparison and improvement of different agent configurations or models.

When to use it

• Comparing Agent Performance: Evaluate multiple agents performing the same task to determine which performs best according to defined metrics.
• Debugging Agent Issues: Identify specific areas where an agent is struggling by analyzing its performance against detailed evaluation criteria.
• Tracking Progress During Development: Monitor improvements in agent capabilities over time as changes are made and retrained.
• Validating New Agent Versions: Ensure that updates or new versions of an agent maintain or improve upon existing performance levels.

Key capabilities

• Define custom evaluation criteria.
• Run agent executions against tasks.
• Systematic assessment of results based on defined criteria.
• Objective comparison of agents.

Example prompts

• "Evaluate Agent A and Agent B on the 'summarization' task using these criteria: accuracy, conciseness, and clarity."
• "Run Agent C through the 'question answering' benchmark and score its performance based on the provided rubric."
• “Compare agent versions 1.0 and 2.0 against the ‘code generation’ test suite.”

Tips & gotchas

To get the most out of this skill, ensure you have clearly defined evaluation criteria before running any assessments. The quality of your evaluation depends directly on the specificity and relevance of those criteria.