Agent Benchmarking
Learn to measure and compare AI agent performance using standardized benchmarks
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0 / 5 completedHow to Run Benchmarks
Running benchmarks is straightforward: load the benchmark suite, connect your agent, execute tasks, and collect results. Most benchmark libraries automate evaluation, so you focus on improving your agent, not building tests. Here's the step-by-step process.
Interactive: Step-by-Step Walkthrough
Click through each step to see the code and expected output:
Install Benchmark Library
First, install the benchmark framework you want to use (e.g., HumanEval).
pip install human-eval
# or
pip install lm-evaluation-harnessSuccessfully installed human-eval-1.0.0Interactive: Benchmark Simulation
Simulate a benchmark run to see real-time progress tracking:
Best Practices for Running Benchmarks
- •Use Production Config: Test with the same settings (temperature, model) users will see
- •Run Multiple Times: Variance happens—average 3-5 runs for reliability
- •Control Costs: Full benchmarks can be expensive—start with subsets
- •Track Over Time: Re-run benchmarks after changes to measure improvement
Set up CI/CD pipelines to run benchmarks automatically on every major change. This catches regressions early and creates a performance history. Many teams run nightly benchmarks and get alerts if scores drop significantly, ensuring quality stays high as development continues.