Agent Seer: Synthesizing Scenarios from Specification Understanding

Researchers have introduced Agent Seer, a framework designed to automate the creation of realistic evaluation scenarios for AI agents that interact with external tools. By synthesizing complex, multi-turn test cases, the system aims to overcome the scalability limitations and labor intensity of manual testing. This approach seeks to provide a more consistent method for measuring how agents perform when executing multi-step tasks in professional environments.