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James Karanja edited this page May 7, 2026 · 1 revision

Agent Evaluation

The adk-eval crate provides comprehensive tools for testing and validating agent behavior. Unlike traditional software testing, agent evaluation must account for the probabilistic nature of LLMs while still providing meaningful quality signals.

Overview

Agent evaluation in ADK-Rust supports multiple evaluation strategies:

  • Trajectory Evaluation: Validate that agents call expected tools in the correct sequence
  • Response Similarity: Compare agent responses using various algorithms (Jaccard, Levenshtein, ROUGE)
  • LLM-Judged Evaluation: Use another LLM to assess semantic similarity and quality
  • Rubric-Based Scoring: Evaluate against custom criteria with weighted scoring

Quick Start

use adk_eval::{Evaluator, EvaluationConfig, EvaluationCriteria};
use std::sync::Arc;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create your agent
    let agent = create_my_agent()?;

    // Configure evaluator with criteria
    let config = EvaluationConfig::with_criteria(
        EvaluationCriteria::exact_tools()
            .with_response_similarity(0.8)
    );

    let evaluator = Evaluator::new(config);

    // Run evaluation against test file
    let report = evaluator
        .evaluate_file(agent, "tests/my_agent.test.json")
        .await?;

    // Check results
    if report.all_passed() {
        println!("All {} tests passed!", report.summary.total);
    } else {
        println!("{}", report.format_summary());
    }

    Ok(())
}

Test File Format

Test cases are defined in JSON files with the .test.json extension:

{
  "eval_set_id": "weather_agent_tests",
  "name": "Weather Agent Tests",
  "description": "Test weather agent functionality",
  "eval_cases": [
    {
      "eval_id": "test_current_weather",
      "conversation": [
        {
          "invocation_id": "inv_001",
          "user_content": {
            "parts": [{"text": "What's the weather in NYC?"}],
            "role": "user"
          },
          "final_response": {
            "parts": [{"text": "The weather in NYC is 65°F and sunny."}],
            "role": "model"
          },
          "intermediate_data": {
            "tool_uses": [
              {
                "name": "get_weather",
                "args": {"location": "NYC"}
              }
            ]
          }
        }
      ]
    }
  ]
}

Evaluation Criteria

Tool Trajectory Matching

Validates that agents call expected tools in the correct order:

let criteria = EvaluationCriteria {
    tool_trajectory_score: Some(1.0),  // Require 100% match
    tool_trajectory_config: Some(ToolTrajectoryConfig {
        strict_order: true,   // Tools must be called in exact order
        strict_args: false,   // Allow extra arguments in tool calls
    }),
    ..Default::default()
};

Options:

  • strict_order: Require exact sequence matching
  • strict_args: Require exact argument matching (no extra args allowed)
  • Partial matching with configurable thresholds

Response Similarity

Compare response text using various algorithms:

let criteria = EvaluationCriteria {
    response_similarity: Some(0.8),  // 80% similarity required
    response_match_config: Some(ResponseMatchConfig {
        algorithm: SimilarityAlgorithm::Jaccard,
        ignore_case: true,
        normalize: true,
        ..Default::default()
    }),
    ..Default::default()
};

Available algorithms:

Algorithm Description
Exact Exact string match
Contains Substring check
Levenshtein Edit distance
Jaccard Word overlap (default)
Rouge1 Unigram overlap
Rouge2 Bigram overlap
RougeL Longest common subsequence

LLM-Judged Semantic Matching

Use an LLM to evaluate semantic equivalence:

use adk_eval::{Evaluator, EvaluationConfig, EvaluationCriteria, LlmJudge};
use adk_model::GeminiModel;

// Create evaluator with LLM judge
let judge_model = Arc::new(GeminiModel::new(&api_key, "gemini-2.5-flash")?);
let config = EvaluationConfig::with_criteria(
    EvaluationCriteria::semantic_match(0.85)
);
let evaluator = Evaluator::with_llm_judge(config, judge_model);

The LLM judge assesses:

  • Semantic equivalence (same meaning, different words)
  • Factual accuracy
  • Completeness of response

Rubric-Based Evaluation

Evaluate against custom criteria with weighted scoring:

use adk_eval::{Rubric, EvaluationCriteria};

let criteria = EvaluationCriteria::default()
    .with_rubrics(0.7, vec![
        Rubric::new("Accuracy", "Response is factually correct")
            .with_weight(0.5),
        Rubric::new("Helpfulness", "Response addresses user's needs")
            .with_weight(0.3),
        Rubric::new("Clarity", "Response is clear and well-organized")
            .with_weight(0.2),
    ]);

Each rubric is scored 0-1 by the LLM judge, then combined using weights.

Safety and Hallucination Detection

Check responses for safety issues and hallucinations:

let criteria = EvaluationCriteria {
    safety_score: Some(0.95),        // Require high safety score
    hallucination_score: Some(0.9),  // Require low hallucination rate
    ..Default::default()
};

Result Reporting

The evaluation report provides detailed results:

let report = evaluator.evaluate_file(agent, "tests/agent.test.json").await?;

// Summary statistics
println!("Total: {}", report.summary.total);
println!("Passed: {}", report.summary.passed);
println!("Failed: {}", report.summary.failed);
println!("Pass Rate: {:.1}%", report.summary.pass_rate * 100.0);

// Detailed failures
for result in report.failures() {
    println!("Failed: {}", result.eval_id);
    for failure in &result.failures {
        println!("  - {}: {} (expected: {}, actual: {})",
            failure.criterion,
            failure.message,
            failure.expected,
            failure.actual
        );
    }
}

// Export to JSON for CI/CD
let json = report.to_json()?;
std::fs::write("eval_results.json", json)?;

Batch Evaluation

Parallel Evaluation

Evaluate multiple test cases concurrently:

let results = evaluator
    .evaluate_cases_parallel(agent, &cases, 4)  // 4 concurrent evaluations
    .await;

Directory Evaluation

Evaluate all test files in a directory:

let reports = evaluator
    .evaluate_directory(agent, "tests/eval_cases")
    .await?;

for (file, report) in reports {
    println!("{}: {} passed, {} failed",
        file,
        report.summary.passed,
        report.summary.failed
    );
}

Integration with cargo test

Use evaluation in standard Rust tests:

#[tokio::test]
async fn test_weather_agent() {
    let agent = create_weather_agent().unwrap();
    let evaluator = Evaluator::new(EvaluationConfig::with_criteria(
        EvaluationCriteria::exact_tools()
    ));

    let report = evaluator
        .evaluate_file(agent, "tests/weather_agent.test.json")
        .await
        .unwrap();

    assert!(report.all_passed(), "{}", report.format_summary());
}

Examples

Use the standard feature tier for evaluation APIs:

cargo check -p adk-rust --no-default-features --features standard

The runnable evaluation gallery is maintained in adk-playground.

Best Practices

  1. Start Simple: Begin with trajectory validation before adding semantic checks
  2. Use Representative Cases: Test files should cover edge cases and common scenarios
  3. Calibrate Thresholds: Start with lenient thresholds and tighten as agent improves
  4. Combine Criteria: Use multiple criteria for comprehensive evaluation
  5. Version Test Files: Keep test files in version control alongside agent code
  6. CI/CD Integration: Run evaluations in CI to catch regressions

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