Judge Concept

The judge concept represents the next evolution of result validation in Spring AI Agents.

1. Overview

Currently, Spring AI Bench uses a simple SuccessVerifier that runs shell commands to validate agent execution results. This approach works for basic scenarios but lacks the sophistication needed for complex enterprise development tasks.

The judge concept will provide comprehensive result validation capabilities that understand the semantic meaning of agent outputs, not just their surface-level characteristics.

2. Vision

2.1. Current State: Command-Based Verification

Today’s verification relies on simple command execution:

success:
  cmd: mvn test
  expectExitCode: 0

This approach can only verify: * Exit codes * File existence * Basic text matching

2.2. Future State: Semantic Judgment

The judge concept will enable sophisticated validation:

@Component
public class CodeQualityJudge implements AgentJudge {

    @Override
    public JudgmentResult evaluate(AgentResult result, JudgmentCriteria criteria) {
        return JudgmentResult.builder()
            .functionalCorrectness(assessFunctionality(result))
            .codeQuality(assessQuality(result))
            .testCoverage(assessCoverage(result))
            .securityCompliance(assessSecurity(result))
            .buildHealth(assessBuild(result))
            .build();
    }
}

3. Key Capabilities

3.1. Multi-Dimensional Assessment

Judges will evaluate multiple aspects of agent work:

  • Functional Correctness - Does the code work as intended?

  • Code Quality - Does it follow best practices and conventions?

  • Test Coverage - Are appropriate tests included?

  • Security Compliance - Does it meet security standards?

  • Performance Impact - Does it introduce performance issues?

  • Documentation Quality - Is it properly documented?

3.2. Deterministic vs AI-Powered Judges

The judge concept supports both deterministic and AI-powered validation approaches:

3.2.1. Deterministic Judges

For measurable, objective criteria that can be verified programmatically:

@Component
public class TestCoverageJudge implements AgentJudge {

    @Override
    public JudgmentResult evaluate(AgentResult result, JudgmentCriteria criteria) {
        // Run coverage analysis
        CoverageReport coverage = coverageAnalyzer.analyze(result.getWorkspace());

        // Apply deterministic rules
        boolean meetsThreshold = coverage.getLineCoverage() >= criteria.getMinCoverage();
        boolean hasNewTests = coverage.getNewTests().size() > 0;

        return JudgmentResult.builder()
            .score(meetsThreshold && hasNewTests ? 1.0 : 0.0)
            .evidence(coverage)
            .deterministic(true)
            .build();
    }
}

Examples of deterministic validation: * Build Success - mvn clean compile exit code * Test Execution - Test suite pass/fail rates * Code Coverage - Line/branch coverage percentages * Static Analysis - Checkstyle, SpotBugs, SonarQube metrics * Performance Benchmarks - Response time, memory usage measurements * Security Scans - Vulnerability detection results

3.2.2. AI-Powered Judges

For subjective criteria requiring semantic understanding:

@Component
public class PRReviewJudge implements AgentJudge {

    @Override
    public JudgmentResult evaluate(AgentResult result, JudgmentCriteria criteria) {
        // Use AI to assess subjective qualities
        String prompt = """
            Review this pull request for:
            - Code readability and maintainability
            - Adherence to project conventions
            - Potential impact on system architecture
            - Overall code quality and best practices

            Changes: %s
            """.formatted(result.getChanges());

        AIResponse assessment = aiClient.call(prompt);

        return JudgmentResult.builder()
            .score(assessment.getQualityScore())
            .feedback(assessment.getFeedback())
            .suggestions(assessment.getSuggestions())
            .deterministic(false)
            .build();
    }
}

Examples of AI-powered validation: * Code Review Quality - Assessing readability, maintainability, design patterns * API Design - Evaluating consistency, usability, and conventions * Architecture Impact - Understanding system-wide implications of changes * Documentation Quality - Assessing clarity, completeness, and usefulness * Domain Appropriateness - Ensuring solutions fit business requirements * User Experience - Evaluating interface design and user workflows

3.3. Context-Aware Validation

Judges will understand the broader context:

  • Project Conventions - Respect existing code styles and patterns

  • Domain Knowledge - Apply domain-specific validation rules

  • Historical Context - Learn from previous validations

  • Integration Impact - Assess effects on other system components

3.4. Composable Judgment Pipeline

Combine deterministic and AI-powered judges for comprehensive assessment:

JudgmentPipeline pipeline = JudgmentPipeline.builder()
    // Deterministic judges (fast, objective)
    .judge(new TestCoverageJudge())           // Measures coverage %
    .judge(new BuildHealthJudge())            // Checks compilation success
    .judge(new StaticAnalysisJudge())         // Runs checkstyle, spotbugs
    .judge(new SecurityScanJudge())           // Vulnerability detection

    // AI-powered judges (slower, subjective)
    .judge(new CodeReviewJudge())             // Assesses readability, design
    .judge(new ArchitectureImpactJudge())     // Evaluates system implications
    .judge(new DocumentationQualityJudge())   // Reviews documentation clarity

    .aggregationStrategy(WeightedAverageStrategy.builder()
        // Weight deterministic judges for gate-keeping
        .weight(TestCoverageJudge.class, 0.25)
        .weight(BuildHealthJudge.class, 0.25)
        // Weight AI judges for quality insights
        .weight(CodeReviewJudge.class, 0.3)
        .weight(ArchitectureImpactJudge.class, 0.2)
        .build())
    .build();

3.4.1. Hybrid Judgment Strategy

For complex scenarios like PR review, combine both approaches:

@Component
public class ComprehensivePRJudge implements AgentJudge {

    @Override
    public JudgmentResult evaluate(AgentResult result, JudgmentCriteria criteria) {
        // 1. Deterministic checks (must pass)
        var deterministicResults = runDeterministicChecks(result);
        if (!deterministicResults.allPassed()) {
            return JudgmentResult.rejected(deterministicResults.getFailures());
        }

        // 2. AI-powered assessment (for quality insights)
        var qualityAssessment = runAIQualityReview(result, criteria);

        // 3. Combine results
        return JudgmentResult.builder()
            .deterministicResults(deterministicResults)
            .qualityAssessment(qualityAssessment)
            .overallScore(calculateOverallScore(deterministicResults, qualityAssessment))
            .build();
    }
}

4. Implementation Roadmap

4.1. Phase 1: Judge Framework Foundation

  • Define core AgentJudge interface

  • Implement JudgmentResult and JudgmentCriteria value objects

  • Create judgment pipeline infrastructure

  • Basic Spring Boot auto-configuration

4.2. Phase 2: Core Judges

  • Functional Correctness Judge - Test execution and validation

  • Code Quality Judge - Static analysis and style checking

  • Build Health Judge - Compilation and dependency validation

  • Basic Security Judge - Common vulnerability patterns

4.3. Phase 3: Advanced Judges

  • Performance Impact Judge - Benchmark and profiling analysis

  • Documentation Quality Judge - README, Javadoc, and comment assessment

  • Integration Impact Judge - Cross-system compatibility analysis

  • Domain-Specific Judges - Industry or technology-specific validation

4.4. Phase 4: AI-Powered Judgment

  • Semantic Understanding - Use AI to understand code intent and quality

  • Learning Judges - Adapt judgment criteria based on project patterns

  • Natural Language Feedback - Generate human-readable improvement suggestions

  • Continuous Learning - Improve judgment accuracy over time

5. Integration Points

5.1. Spring AI Bench Migration

The judge concept will replace Spring AI Bench’s SuccessVerifier:

// Current: Simple command-based verification
SuccessVerifier verifier = new SuccessVerifier();
boolean success = verifier.verify(workspace, successSpec, timeout);

// Future: Comprehensive semantic judgment
AgentJudge judge = new ComprehensiveJudge();
JudgmentResult judgment = judge.evaluate(agentResult, judgmentCriteria);

5.2. AgentClient Integration

Judges will integrate seamlessly with the AgentClient API:

AgentResult result = agentClient
    .goal("Fix the authentication bug in UserService")
    .workspace(projectPath)
    .judge(codeQualityJudge)
    .call();

JudgmentResult judgment = result.getJudgment();
if (judgment.isAcceptable()) {
    // Proceed with the changes
} else {
    // Review suggested improvements
    judgment.getSuggestions().forEach(System.out::println);
}

6. Benefits

6.1. For Developers

  • Quality Assurance - Automated validation ensures high code quality

  • Learning Tool - Judgment feedback helps improve development practices

  • Time Savings - Reduces manual code review overhead

  • Consistency - Ensures consistent quality standards across projects

6.2. for Organizations

  • Risk Mitigation - Prevents low-quality code from entering production

  • Compliance - Ensures adherence to security and regulatory standards

  • Knowledge Transfer - Codifies organizational best practices

  • Scalability - Enables high-quality AI agent deployment at scale

7. Related Concepts

  • Context Engineering - Provides judges with rich project context for better validation

  • Agent Orchestration - Coordinates multiple agents with appropriate judges

  • Feedback Loops - Uses judgment results to improve agent performance over time

The judge concept represents a fundamental shift from simple verification to comprehensive quality assessment, enabling Spring AI Agents to deliver enterprise-grade results that meet the highest standards of professional software development.