
About
Testing and benchmarking LLM agents including behavioral testing,
name: agent-evaluation description: 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 risk: safe source: vibeship-spawner-skills (Apache 2.0) date_added: 2026-02-27
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;
async evaluateAgent(
agent: Agent,
testSuite: TestCase[]
): Promise<EvaluationReport> {
const results: TestResult[] = [];
// Run each test multiple times
for (const test of testSuite) {
for (let run = 0; run < this.minRuns; run++) {
const result = await this.runTest(agent, test, run);
results.push(result);
}
}
// Analyze by test
const byTest = this.groupByTest(results);
const testAnalyses = new Map<string, StatisticalAnalysis>();
for (const [testId, testResults] of byTest) {
testAnalyses.set(testId, this.analyzeResults(testResults));
}
// Overall analysis
const overall = this.analyzeResults(results);
return {
overall,
byTest: testAnalyses,
concerns: this.identifyConcerns(testAnalyses),
recommendations: this.generateRecommendations(testAnalyses)
};
}
private analyzeResults(results: TestResult[]): StatisticalAnalysis {
const passes = results.filter(r => r.passed);
const passRate = passes.length / results.length;
// Calculate confidence interval for pass rate
const z = 1.96; // 95% confidence
const se = Math.sqrt((passRate * (1 - passRate)) / results.length);
const confidence95: [number, number] = [
Math.max(0, passRate - z * se),
Math.min(1, passRate + z * se)
];
const scores = results.map(r => r.score);
const latencies = results.map(r => r.latencyMs);
return {
passRate,
confidence95,
meanScore: this.mean(scores),
stdDevScore: this.stdDev(scores),
meanLatency: this.mean(latencies),
p95Latency: this.percentile(latencies, 95),
behaviorConsistency: this.calculateConsistency(results)
};
}
private calculateConsistency(results: TestResult[]): number {
// How consistent are the behaviors across runs?
if (results.length < 2) return 1;
const behaviorSets = results.map(r => new Set(r.actualBehaviors));
let consistencySum = 0;
let comparisons = 0;
for (let i = 0; i < behaviorSets.length; i++) {
for (let j = i + 1; j < behaviorSets.length; j++) {
const intersection = new Set(
[...behaviorSets[i]].filter(x => behaviorSets[j].has(x))
);
const union = new Set([...behaviorSets[i], ...behaviorSets[j]]