Agent Evals

What it does

The agent-evals skill provides capabilities for evaluating the performance of AI agents. It allows users to define evaluation criteria and metrics, then assesses agent outputs against these standards. This enables iterative improvement and ensures agents are meeting desired objectives.

When to use it

• Testing new agent workflows: Evaluate a newly built agent's ability to complete tasks before deployment.
• Comparing different agent designs: Determine which architecture or prompt engineering approach yields the best results.
• Monitoring ongoing agent performance: Track key metrics over time to identify degradation and trigger retraining.
• Benchmarking against competitors: Assess your agents’ capabilities relative to alternatives in the market.

Key capabilities

• Define evaluation criteria.
• Specify evaluation metrics.
• Assess agent outputs.
• Provide iterative improvement feedback.

Example prompts

• "Evaluate this agent's response: [agent output] against these criteria: accuracy, completeness, and clarity."
• "Give me a score for the agent’s performance on summarizing this document: [document content]."
• "Compare the outputs of Agent A and Agent B when asked to write a Python function for sorting a list. Evaluate based on efficiency and correctness."

Tips & gotchas

The effectiveness of agent-evals depends heavily on clearly defined evaluation criteria and appropriate metrics. Vague or poorly chosen standards will lead to unreliable assessments.