agent-evaluation

Agent Evaluation

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks

Capabilities

• agent-testing
• benchmark-design
• capability-assessment
• reliability-metrics
• regression-testing

Prerequisites

• Knowledge: Testing methodologies, Statistical analysis basics, LLM behavior patterns
• Skills_recommended: autonomous-agents, multi-agent-orchestration
• Required skills: testing-fundamentals, llm-fundamentals

Scope

• Does_not_cover: Model training evaluation (loss, perplexity), Fairness and bias testing, User experience testing
• Boundaries: Focus is agent capability and reliability, Covers functional and behavioral testing

Ecosystem

Primary_tools

• AgentBench - Multi-environment benchmark for LLM agents (ICLR 2024)
• τ-bench (Tau-bench) - Sierra's real-world agent benchmark
• ToolEmu - Risky behavior detection for agent tool use
• Langsmith - LLM tracing and evaluation platform

Alternatives

• Braintrust - When: Need production monitoring integration LLM evaluation and monitoring
• PromptFoo - When: Focus on prompt-level evaluation Prompt testing framework

Deprecated

• Manual testing only

Patterns

Statistical Test Evaluation

Run tests multiple times and analyze result distributions

When to use: Evaluating stochastic agent behavior

interface TestResult { testId: string; runId: string; passed: boolean; score: number; // 0-1 for partial credit latencyMs: number; tokensUsed: number; output: string; expectedBehaviors: string[]; actualBehaviors: string[]; }

interface StatisticalAnalysis { passRate: number; confidence95: [number, number]; meanScore: number; stdDevScore: number; meanLatency: number; p95Latency: number; behaviorConsistency: number; }

class StatisticalEvaluator { private readonly minRuns = 10; private readonly confidenceLevel = 0.95;