Customize and streamline your agent evaluations
Create custom agent evaluation datasets with mocked APIs, databases, and custom file systems, and run them in a fully managed sandboxed environment uniquely configured to match your production settings, with full tracing and actionable insights.
Build Realistic Testbeds, Automatically
Design realistic test scenarios. Simulate every interface your agent interacts with—databases, external APIs, MCP servers, and file systems—all automatically set up and torn down for each test.
Fully Managed Test Orchestration
Bring your simulation image—we’ll handle the rest. We provision all the resources needed to orchestrate your tests, collect traces, and generate reports.
Turn Data into Actionable Insights
Gain more than just a success-or-fail outcome—unlock deep, organized data and powerful visuals that empower you to evaluate and analyze agent performance faster and more effectively. Make smarter decisions with clarity and ease.
Decision Path
Use SDK to record steps taken by your agent.
Files Explored
Record files and data retrieved by your agent.
Your Output vs Expected Output
Compare the output generated by your agent with the expected output.
Raw Execution Logs
Download execution logs if you ever need to dive deep.
Advanced Insights
Traditional benchmarks provide just a single accuracy metric—but that’s not enough to truly understand your AI agent. We go further, delivering a wealth of additional metrics and in-depth insights to help you analyze behavior, uncover hidden issues, and fine-tune performance with precision.
Versioned Experiments
Effortlessly track and organize your experiment history. Keep every report at your fingertips and link results directly to the exact version of your experiment code—so you never lose valuable insights.
Easy Setup
The only thing you need to do is to handle a single task instance. Benchx takes care of feeding benchmark tasks to your code using isolated containers.
Ready to Understand More About Your Agent?
Stay ahead of the competition with continuous benchmarking and improvement cycles. Use benchmarks to run controlled experiments, collect deep insights, and accelerate iterations.