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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]]