Agent Evaluation

Agent evaluation provides systematic approaches to testing and validating agent performance, moving beyond prototype to production-ready AI systems.

Overview

Unlike traditional software testing, agent evaluation must account for the probabilistic nature of LLM responses and the complexity of multi-step reasoning processes. Effective evaluation encompasses multiple dimensions from tool usage patterns to response quality.

Current Capabilities

While the full evaluation framework is in development, you can currently:

• Manual Testing: Use the examples to test agent behavior manually
• Session Analysis: Review agent interactions through session management
• Response Validation: Manually verify agent outputs and tool usage
• Performance Observation: Monitor agent behavior through logging and events

Documentation Structure

Coming Features

The upcoming evaluation framework will include:

• AgentEvaluator: Comprehensive agent performance assessment
• TrajectoryEvaluator: Tool usage and decision path analysis
• ResponseEvaluator: Output quality and semantic similarity scoring
• EvalSet Management: Batch evaluation of complex scenarios
• Automated Test Runners: Continuous integration with development workflows
• Performance Analytics: Trend analysis and regression detection

Getting Started

For immediate testing needs:

1. Review Examples: Explore the examples directory for agent testing patterns
2. Session Monitoring: Use session services to track agent interactions
3. Manual Validation: Create custom test scripts using the Runner class
4. Event Analysis: Monitor agent events for behavior analysis

Related Topics