Comprehensive testing and evaluation framework for AI agent performance and reliability.
A framework for testing whether AI agents actually accomplish their goals — measure performance before deploying to production.
Agent Eval is a specialized testing framework designed specifically for evaluating AI agent performance, reliability, and safety. Unlike traditional software testing tools, Agent Eval understands the unique challenges of testing non-deterministic AI systems and provides metrics and methodologies tailored for agent evaluation.
The framework supports multiple evaluation methodologies including benchmark testing against standard datasets, regression testing for consistent behavior, and adversarial testing for robustness. It includes built-in support for evaluating multi-agent systems, conversation quality, and tool usage effectiveness.
Key capabilities include automated test generation based on agent capabilities, performance regression detection, and safety evaluation for identifying harmful or incorrect behaviors. The framework can simulate various conditions including API failures, network issues, and edge cases that agents might encounter in production.
Agent Eval provides comprehensive reporting with visualizations for test results, trend analysis, and comparison across agent versions. It integrates with CI/CD pipelines for continuous agent evaluation and includes benchmarking against industry-standard agent performance metrics.
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AI-powered test case generation that creates comprehensive test suites based on agent capabilities and use cases.
Use Case:
Testing complex agents with many tools and capabilities without manually writing hundreds of test cases.
Built-in support for standard agent benchmarks like SWE-bench, HumanEval, and custom domain-specific evaluations.
Use Case:
Comparing agent performance against industry standards and tracking improvements over time.
Specialized testing for multi-agent systems including coordination evaluation, conversation quality, and collaboration effectiveness.
Use Case:
Ensuring multi-agent teams work together effectively and produce coherent, high-quality outputs.
Adversarial testing, jailbreaking attempts, and edge case evaluation to identify potential safety issues and failure modes.
Use Case:
Production safety validation for agents that handle sensitive data or high-stakes decisions.
Automated detection of performance degradation across agent versions with statistical significance testing.
Use Case:
Continuous integration pipelines that need to catch performance regressions before deployment.
Detailed analytics with trend analysis, performance comparisons, and exportable reports for stakeholder communication.
Use Case:
Demonstrating agent quality improvements to stakeholders and tracking development progress.
Free
month
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View Pricing Options →Production agent quality assurance
Continuous integration testing
Agent performance benchmarking
Safety and robustness validation
We believe in transparent reviews. Here's what Agent Eval doesn't handle well:
Agent Eval works with any agent that can be called via API or Python interface, including LangChain, CrewAI, AutoGen, and custom implementations.
Yes, the platform supports custom metrics, benchmarks, and evaluation criteria tailored to your specific use case.
Statistical testing methods, multiple evaluation runs, and fuzzy matching to handle the inherent variability in AI agent outputs.
Yes, with specialized tools for evaluating agent coordination, conversation quality, and collaborative task completion.
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