5 min
ai
February 8, 2026

Evaluation Frameworks for Agentic Systems

title: "Evaluation Frameworks for Agentic Systems" description: "Research on measuring agent performance, quality assessment, and evaluation methodologies" date: 2026-02-06 topics: [evaluation, metrics, assessment, benchmarking] sources: 0 status: initial

Evaluation Frameworks for Agentic Systems

Overview

Evaluation of AI agents in software development requires multi-dimensional assessment beyond traditional code metrics. This research covers evaluation frameworks, benchmarks, and quality assessment methodologies for agentic systems.

Dimensions of Evaluation

1. Task Completion Evaluation

Functional Correctness

typescript
interface CorrectnessEvaluation {
  // Does the output meet requirements?
  requirementCoverage: number;      // % of requirements addressed
  acceptanceCriteriaPass: boolean;   // All AC met?
  edgeCaseHandling: number;         // Edge cases covered
  
  // Validation methods
  testPassRate: number;             // Automated tests
  staticAnalysisScore: number;      // Lint, type check
  runtimeValidation: boolean;       // Does it run?
}

Semantic Correctness

  • Business logic alignment
  • Domain appropriateness
  • User intent satisfaction
  • Contextual relevance

2. Code Quality Evaluation

Traditional Metrics

typescript
interface CodeQualityMetrics {
  // Complexity
  cyclomaticComplexity: number;
  cognitiveComplexity: number;
  linesOfCode: number;
  
  // Maintainability
  codeDuplication: number;          // % duplicated
  testCoverage: number;             // Line/branch coverage
  documentationCoverage: number;    // % documented
  
  // Reliability
  errorHandlingCoverage: number;    // % paths with handling
  typeSafety: number;               // Type coverage
  nullSafety: number;               // Null check coverage
}

AI-Specific Quality Indicators

typescript
interface AgentCodeQuality {
  // Generation quality
  hallucinationRate: number;        // Fake APIs/imports
  repetitionRate: number;           // Copy-paste code
  coherenceScore: number;           // Logical consistency
  
  // Context utilization
  requirementAdherence: number;     // Stuck to spec?
  constraintSatisfaction: number;   // Respected limits?
  patternConsistency: number;       // Followed conventions?
}

3. Process Efficiency Evaluation

Resource Utilization

typescript
interface EfficiencyMetrics {
  // Time
  timeToCompletion: number;         // Wall clock time
  activeProcessingTime: number;     // CPU time
  waitTime: number;                 // I/O blocking
  
  // Cost
  apiCalls: number;                 // LLM calls
  tokensConsumed: number;           // Input + output
  estimatedCost: number;            // $ spent
  
  // Iterations
  attempts: number;                 // Retry count
  correctionCycles: number;         // Fix iterations
  humanInterventions: number;       // Times human needed
}

Comparative Efficiency

  • vs human baseline (time/cost)
  • vs previous agent versions
  • vs industry benchmarks

4. Safety & Reliability Evaluation

Robustness Testing

typescript
interface RobustnessEvaluation {
  // Input variations
  adversarialRobustness: number;    // Handles bad inputs?
  ambiguityHandling: number;        // Clarifies vs assumes?
  
  // Failure modes
  gracefulDegradation: boolean;     // Fails safely?
  errorRecoveryRate: number;        // Self-fixes errors?
  
  // Security
  injectionResistance: number;      // Resists prompt injection
  secretExposure: boolean;          // Leaked credentials?
}

Evaluation Methodologies

1. Automated Benchmarking

Static Benchmark Suites

typescript
interface BenchmarkSuite {
  name: string;
  tasks: BenchmarkTask[];
  
  async run(agent: Agent): Promise<BenchmarkResult> {
    const results = [];
    
    for (const task of this.tasks) {
      const start = Date.now();
      const output = await agent.execute(task.input);
      const duration = Date.now() - start;
      
      results.push({
        task: task.id,
        output,
        expected: task.expected,
        score: task.evaluate(output),
        duration
      });
    }
    
    return this.aggregate(results);
  }
}

HumanEval-style Coding Benchmarks

  • Function completion tasks
  • Bug fixing challenges
  • Refactoring exercises
  • Documentation generation

2. Human Evaluation

Expert Review Process

typescript
interface ExpertReview {
  reviewer: Expert;
  dimensions: ReviewDimension[];
  
  async review(output: AgentOutput): Promise<ReviewScore> {
    const scores = {};
    
    for (const dim of this.dimensions) {
      scores[dim.name] = await this.evaluator.rate(
        output,
        dim.criteria,
        dim.rubric
      );
    }
    
    return {
      scores,
      overall: this.weightedAverage(scores),
      feedback: this.generateFeedback(scores)
    };
  }
}

Review Dimensions:

  • Correctness (functional, semantic)
  • Quality (maintainability, readability)
  • Efficiency (resource usage, performance)
  • Safety (security, robustness)
  • Appropriateness (fits context, constraints)

3. A/B Testing

Comparative Evaluation

typescript
interface ABTest {
  control: AgentVersion;
  treatment: AgentVersion;
  
  async run(tasks: Task[], n: number): Promise<ABResult> {
    const results = { control: [], treatment: [] };
    
    for (const task of tasks) {
      for (let i = 0; i < n; i++) {
        results.control.push(
          await this.runTask(this.control, task)
        );
        results.treatment.push(
          await this.runTask(this.treatment, task)
        );
      }
    }
    
    return this.statisticalAnalysis(results);
  }
}

4. Longitudinal Evaluation

Performance Over Time

typescript
interface LongitudinalStudy {
  agent: Agent;
  startDate: Date;
  
  async track(metrics: Metric[]): Promise<TrendAnalysis> {
    const data = await this.collectHistoricalData();
    
    return {
      trends: this.calculateTrends(data),
      regressions: this.detectRegressions(data),
      improvements: this.identifyImprovements(data),
      volatility: this.calculateVolatility(data)
    };
  }
}

Metrics to Track:

  • Success rate trends
  • Cost per task trends
  • Quality score trends
  • Error pattern evolution

Evaluation Frameworks

1. Rubric-Based Scoring

Multi-Dimensional Rubric

typescript
interface EvaluationRubric {
  dimensions: {
    name: string;
    weight: number;
    criteria: Criterion[];
  }[];
  
  evaluate(output: AgentOutput): Score {
    const scores = this.dimensions.map(d => ({
      dimension: d.name,
      score: d.criteria.reduce((sum, c) => 
        sum + c.evaluate(output), 0
      ) / d.criteria.length,
      weight: d.weight
    }));
    
    return {
      breakdown: scores,
      overall: this.weightedAverage(scores)
    };
  }
}

2. Reference-Based Evaluation

Golden Dataset Comparison

typescript
interface ReferenceEvaluation {
  async evaluateAgainstReference(
    output: AgentOutput,
    reference: ReferenceOutput
  ): Promise<SimilarityScore> {
    return {
      functional: this.compareBehavior(output, reference),
      structural: this.compareStructure(output, reference),
      semantic: await this.embeddingsSimilarity(output, reference)
    };
  }
}

3. Task-Specific Metrics

Code Generation

  • Compilation success rate
  • Test pass rate
  • Runtime performance
  • Memory efficiency

Code Review

  • True positive rate (real issues caught)
  • False positive rate (noise)
  • Actionability (clear fixes suggested)
  • Severity accuracy

Testing

  • Coverage increase
  • Bug detection rate
  • Test quality (mutation score)
  • Flakiness rate

Benchmark Datasets

1. SWE-Bench Style

Real GitHub issues with:

  • Issue description
  • Repository state
  • Expected fix
  • Validation tests

2. HumanEval Extended

Function-level coding tasks:

  • Docstring to implementation
  • Multiple language support
  • Difficulty levels

3. Domain-Specific

  • Frontend component generation
  • API endpoint implementation
  • Database schema design
  • DevOps pipeline creation

Evaluation Infrastructure

1. Continuous Evaluation

typescript
interface ContinuousEvaluator {
  async evaluateCommit(commit: Commit): Promise<EvalResult> {
    // Run benchmarks
    const benchmarkResults = await this.runBenchmarks(commit);
    
    // Compare to baseline
    const comparison = await this.compareToBaseline(
      commit,
      this.getBaseline()
    );
    
    // Check for regressions
    const regressions = this.detectRegressions(comparison);
    
    return {
      benchmarkResults,
      comparison,
      regressions,
      recommendation: this.generateRecommendation(regressions)
    };
  }
}

2. Evaluation Dashboard

Key Visualizations:

  • Success rate over time
  • Quality score distributions
  • Cost per task trends
  • Regression heatmaps
  • Agent comparison matrices

3. Feedback Loop Integration

typescript
interface EvalFeedbackLoop {
  async incorporateFeedback(
    evaluation: EvaluationResult,
    feedback: HumanFeedback
  ): Promise<void> {
    // Update evaluation weights
    this.adjustWeights(feedback.disagreements);
    
    // Add to training data
    this.addToTrainingSet(evaluation, feedback);
    
    // Update rubrics
    this.refineRubrics(feedback.suggestions);
    
    // Trigger re-evaluation if needed
    if (feedback.significant) {
      await this.reEvaluateRecent();
    }
  }
}

Open Questions

  • How to evaluate creativity and innovation?
  • What's the right balance of automated vs human eval?
  • How to handle evaluation of subjective qualities?
  • Standardized benchmarks for agent comparisons?
  • Evaluation cost vs comprehensiveness tradeoffs?