Test AI on real work benchmarks.
Building AI agents for operations? You need custom benchmarks that test them for accuracy, reliability, and safety.
Benchmark your agentWhat to check before an agent does real work.
Whether you're building your own agents or evaluating a vendor's.
- Test on your own tasks, not public benchmarks.
- Test the whole setup: model, instructions, tools, and permissions.
- Check what it does when information is missing or an action is not allowed.
- Retest whenever the model, tools, or procedures change.
Recreate the task as a test.
We recreate tasks like payment reconciliation and compliance reviews in a test environment and define what a correct result looks like.
Example benchmark Reconcile payments.
- Starts with
- Payment records and a ledger.
- A passing run
- Matches the right records
- Flags discrepancies
- Saves the result
Compare models and harnesses.
Run the same tests on the complete agent setup: the model, instructions, tools, permissions, memory, and execution environment.
- Correct results
- Does it complete the task and save the right result?
- Missing information
- Does it ask for review when it cannot finish?
- Working controls
- Do permissions and approvals block unauthorized actions? Does stopping the agent stop its actions?
Train the agent on your workflow.
Computer-use data
Each run produces task inputs, screenshots, and recorded actions.
Custom RL environments
Successful and failed runs both become training tasks.
The best teams test agents the way they test software.
They write tests from their own work, rerun them on every change, and train on both the successes and the failures.