CALVIN SIMULATE
AI agent simulation, starting with your mission.
Describe what your agent needs to accomplish. Calvin Simulate creates scenarios and commitments for you to review, so you can test before launch without becoming an evaluation expert.
From a mission statement to simulation scenarios#
Start with the job, such as “Help patients book the right appointment, easily.” Simulate creates everyday requests and difficult edge cases around that mission. Your team reviews the commitments that define success.
A commitment can cover behavior, such as confirming only after a calendar responds, or an outcome, such as a matching appointment in the calendar. Checking both helps reveal failures a convincing response can hide.
How to test your AI agent with Simulate#
- Describe the mission and connect your agent through a phone number, chat link, or API.
- Review the scenarios and approve commitments against your business rules.
- Run simulations covering ordinary requests, conflicting requirements, and handoffs.
- Review each acceptance result with its evidence. Fix issues and rerun the checks before launch.
Scenarios for chatbots, voice agents, and automation#
For a conversational chatbot, compare the advice with its source data. A coaching bot praising net play should be reviewed when the match data shows low net possession.
For a voice AI booking agent, test whether a spoken confirmation follows a successful calendar entry. Include unavailable appointment slots and requests that need a human handoff.
For a back-office automation, test whether an invoice is marked paid only after the bank confirms settlement. A failed transfer should not produce a successful completion claim.
Know what passed, failed, or remains unverified#
Each commitment gets a clear acceptance result. A matching record can support success; a checked system with a missing required action can establish a failure. When the necessary evidence cannot be obtained, keep the result unverified.
Your team owns the release decision. Review the evidence and unresolved issues before making changes or launching a new version.
Improve simulations with production evidence#
After launch, Observe checks real work and groups recurring problems. Turn those production examples into future Simulate checks so the next release is tested against the problems your users encountered.
Explore Observe for production monitoring →
Learn how Simulate, Observe, and Improve work together →
Use Improve to turn evidence into recommendations#
When Observe finds misalignment with the mission, Improve recommends changes to the agent’s prompt, configuration, or tools. Your team reviews the recommendations and deploys the changes.
Rerun the relevant scenarios in Simulate and use Observe to check the result in production. A recommendation is a proposed change; verified improvement still requires evidence.