agent-evaluation

LLM Agent Evaluation Guide

Skill Overview

Agent Evaluation is a methodology specifically designed for testing and benchmarking LLM agents, covering behavioral testing, capability assessment, reliability metrics, and production monitoring, helping you accurately measure agent performance in real-world scenarios.

Applicable Scenarios

1. Pre-deployment Quality Validation for Agents

Before deploying an agent to production, verify its reliability through behavioral regression testing and capability assessment. This includes statistical testing (running multiple iterations to analyze the distribution of results), behavioral contract testing (verifying invariants in agent behavior), and adversarial testing (actively attempting to break the agent's behavior).

2. Production Monitoring of Agents

Continuously monitor the performance of deployed agents, collect reliability metrics, and detect performance degradation in a timely manner. This helps you identify agents that perform well on benchmarks but fail in real-world scenarios; even top-performing agents often do not reach a 50% pass rate in realistic benchmarks.

3. Agent Benchmark Design

Design and implement agent benchmarks that reflect real use cases, avoiding metric gaming. Use multidimensional evaluation to prevent agents from optimizing for specific metrics at the expense of actual task objectives.

Core Features

Agent Behavioral Testing (agent-testing)

Provides behavioral testing methods tailored to LLM agents. Unlike traditional software testing, agent testing must handle cases where the same input produces different outputs and where "correct" answers are often not unique. Supports multiple testing modes such as statistical testing and adversarial testing.

Agent Capability Assessment (capability-assessment)

Systematically assess agent capabilities across multiple dimensions, including task completion rate, output quality, handling of edge cases, etc. Helps you understand an agent's strengths and limitations and avoid overreliance on a single metric.

Reliability Metrics and Monitoring (reliability-metrics)

Define and track key reliability metrics for agents, including response consistency, error rate, trends in performance degradation, etc. Pay special attention to the gap between benchmark and production performance, offering multidimensional evaluation to prevent agents from being over-optimized for particular metrics.

Frequently Asked Questions

Why does an agent perform well on benchmarks but fail in production?

This is a common phenomenon known as the "benchmark-to-production gap." Benchmarks typically use standardized, static datasets, whereas production environments are full of diversity, edge cases, and unexpected inputs. Agent Evaluation emphasizes bridging this gap through behavioral contract tests and adversarial testing, and recommends using real-world data for testing.

How do LLM agent tests differ from traditional software tests?

Traditional software testing expects the same input to produce the same output, while LLM agents have randomness and creativity — the same input may produce different but equally valid outputs. Therefore, agent testing needs to use statistical methods (running multiple times and analyzing the distribution of results) rather than single-run tests, avoid simple string matching, and focus on behavior patterns rather than exact outputs.

How to prevent test data from leaking into the agent's training data or prompts?

Data leakage is a serious issue and can lead to inflated agent performance during testing. Solutions include: using independent and isolated test datasets, avoiding including test samples in prompts, regularly updating test data, and monitoring whether the agent has become overly sensitive to test patterns. Agent Evaluation flags this as a critical-level risk.