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Research topic
AI evaluation
AI evaluation measures what an LLM agent does with what it receives: whether it completes the task, which tools it calls and how, when it stops and hands off to a person, and whether its decisions hold across repeated runs and model versions. Each measure answers a different question about whether an agent can be trusted with a decision.
Work with us on AI evaluation and simulation
Rillor tests how LLM agents behave before they are trusted with real decisions, in simulated markets and auctions and on an organization's own tasks. Agents are measured through their inputs and outputs alone, on pinned model versions, and every run can be replayed from its log. Before an agent goes live, we score task success on cases with known answers, along with refusal and escalation, tool-use errors and consistency across runs.
Notices
AI outputs can be wrong. Important decisions should include human review.
Test your agent before it goes live.
Send us the tasks your agent handles, the model versions it runs on and the decisions it is trusted with.