MeshGuardEval: A Contract-Driven Evaluation Framework for AI Systems

MeshGuardEval is a contract-driven evaluation framework for AI systems, integrating QA testing, security testing, and AI safety verification. It supports multi-agent workflow validation, unsafe prompt detection, tool invocation behavior analysis, and summary accuracy assessment, generating reproducible and auditable evaluation outputs for government tech departments and AI quality teams. The background is that the deployment of AI systems (especially large language models and intelligent agents) in critical domains poses evaluation challenges. Traditional software testing methods struggle to address their probabilistic, open-ended, and emergent characteristics, leading to the development of this framework.

With the deployment of AI systems (especially large language models and AI agents) in critical domains, how to systematically evaluate their quality, security, and reliability has become an urgent challenge. Traditional software testing methods struggle to address the probabilistic, open-ended, and emergent characteristics of AI systems, so MeshGuardEval provides a contract-driven evaluation framework specifically for AI systems.

MeshGuardEval adopts a contract-driven evaluation concept, verifying the actual performance of AI systems through predefined contracts (expected behavior norms). Contract types include: functional contracts (input/output formats, functional boundaries, performance metrics), security contracts (prohibited behaviors, sensitive information handling, access control), and quality contracts (accuracy thresholds, response time, resource limits). The evaluation process is: Contract Definition → Test Generation → Evaluation Execution → Result Analysis → Report Generation.

1. Multi-agent Workflow Validation: Verify agent communication protocols, detect collaboration failures, assess the rationality of task allocation, and validate final output goals; 2. Unsafe Prompt Detection: Detect vulnerabilities to malicious prompts, verify the effectiveness of safety guardrails, assess boundary behaviors, and generate security reports; 3. Tool Invocation Analysis: Verify parameter compliance, detect improper tool combinations, assess the security of invocation chains, and validate error handling mechanisms; 4. Summary Accuracy Assessment: Reference standard quality evaluation, fact consistency check, information integrity verification, and style compliance assessment.

MeshGuardEval ensures the reproducibility and auditability of evaluation results through the following mechanisms: version control (contracts, test cases, and evaluation scripts are included in version control), environment freezing (recording complete evaluation environment configurations), evidence collection (saving intermediate results and original outputs), and audit logs (recording operation logs of the evaluation process).

1. Government Technology (GovTech): Security assessment of public service chatbots, accuracy verification of policy analysis tools, fairness review of automated decision systems; 2. Enterprise AI Quality Assurance: Comprehensive pre-deployment evaluation, monitoring of behavior changes in production systems, meeting compliance audits; 3. AI Vendor Evaluation: Verifying product capabilities, assessing security risks and quality levels, and serving as a basis for contract acceptance.

MeshGuardEval adopts a modular design: Contract Definition Layer (supports multiple description formats), Test Generator (automatically generates test cases), Execution Engine (supports multiple AI system interfaces), Analyzer (multi-dimensional result analysis), and Report Generator (reports in multiple formats). This framework fills the gap in AI evaluation, provides a systematic, standardized, and auditable method, and becomes a key part of AI governance infrastructure, suitable for government agencies, financial institutions, and large enterprises.