/agent-evaluation

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]]); consistencySum += intersection.size / union.size; comparisons++; } }

return consistencySum / comparisons; }

private identifyConcerns(analyses: Map<string, StatisticalAnalysis>): Concern[] { const concerns: Concern[] = [];

for (const [testId, analysis] of analyses) { if (analysis.passRate < 0.8) { concerns.push({ testId, type: 'low_pass_rate', severity: analysis.passRate < 0.5 ? 'critical' : 'high', message: `Pass rate ${(analysis.passRate * 100).toFixed(1)}% below threshold` }); }

if (analysis.behaviorConsistency < 0.7) { concerns.push({ testId, type: 'inconsistent_behavior', severity: 'high', message: `Behavior consistency ${(analysis.behaviorConsistency *