Reporting outcome metrics
safely
Runs controlled experiments with defined inputs, variations, and evaluation criteria.
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Prompt Experiments
Model Benchmarks
Workflow Testing
Edge-Case Testing
Performance Testing
Tool Comparison
AI Experimentation & Benchmarking Workflow
Define experiment parameters
Run controlled AI workflows
Compare outcomes objectively
Connect all your apps to AI
and orchestrate them from one place
Automate complex workflows across all your tools—seamlessly connecting data, actions, and systems through AI-powered orchestration.
Use it for
Prompt Experiments
Test and compare prompt strategies.
Model Benchmarks
Evaluate outputs across models or configurations.
Workflow Testing
Validate agent workflows before rollout.
Edge-Case Testing
Explore failure modes safely.
Performance Testing
Measure consistency and quality.

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Integrate with 2000+ apps and services
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FAQs
Any more questions?
What kind of experiments can I run? Is it only for software testing?
You can run a wide range of experiments, from testing AI model performance or prompt variations to simulating the impact of different business strategies, like new pricing models, using your data.
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How do I define the success criteria for an experiment?
You define the evaluation criteria in the experiment setup. This could be quantitative metrics like accuracy, conversion rate or qualitative scores like user satisfaction, output quality.
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Does this agent help with A/B testing?
Yes, its perfect for A/B testing and more complex multivariate testing. You can define multiple variations, and the agent will run them in a controlled environment and report on their comparative performance.
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What kind of report does it generate?
It produces a structured experiment report that includes the initial hypothesis, the parameters of each variation, the raw results, and a statistical analysis comparing the outcomes.
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Can it help prevent a bad change from going into production?
Absolutely. By testing new models, prompts, or workflows in a controlled setting, you can validate their performance and safety before deploying them to a production environment, reducing risk.
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Does it require a lot of data to run an experiment?
Not necessarily. The data requirements depend on the experiment. For some tests, like comparing two prompts on a specific task, you might only need a small set of test cases.
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