Agent Evaluation

What it does

This skill, Agent Evaluation, assesses the performance of AI agents using a structured approach that accounts for their non-deterministic nature and lack of single correct answers. It identifies key factors influencing agent effectiveness – primarily token usage, tool calls, and model choice – to provide actionable insights for improvement. The skill uses an LLM-as-Judge system to evaluate agent outputs against ground truth data based on a multi-dimensional rubric.

When to use it

• To understand the primary drivers of agent performance (token usage, tool calls, model selection).
• When comparing different agent strategies or models for a given task.
• To identify and address potential pitfalls like path overfitting or ignoring edge cases in agent behavior.
• For continuous monitoring of agent quality following changes or updates.
• To evaluate the impact of context engineering on agent performance.

Key capabilities

• Multi-Dimensional Rubric: Evaluates agents based on factual accuracy, completeness, citation accuracy, and tool efficiency.
• LLM-as-Judge: Uses an LLM to score agent outputs against ground truth data with reasoning provided.
• Test Set Design: Employs a test set stratified by complexity (simple, medium, complex, very complex) to assess performance across different scenarios.
• Complexity Stratification: Categorizes tasks based on the number of tool calls and level of reasoning required.
• Context Engineering Evaluation: Tests and compares agent performance with different context strategies.
• Continuous Evaluation Pipeline: Automates evaluation runs, tracks metrics over time, and sets alerts for quality drops.

Example prompts

• "Evaluate this agent's response to the prompt: 'What is the capital of France?'"
• "Compare the performance of Agent A and Agent B on the task: 'Analyze Q1-Q4 sales trends.'"
• "Run an evaluation pipeline with a test set including tasks of varying complexity."

Tips & gotchas

• Agent Evaluation focuses on outcomes, not the specific steps taken by the agent.
• Ensure your test sets include diverse scenarios to avoid overlooking edge cases.
• The skill uses token usage as a key performance driver; be mindful of cost implications when increasing token limits.