Gaia2 and ARE: Empowering the Community to Evaluate Agents
In an ideal world, AI agents would be reliable assistants. When given a query, they would easily manage ambiguity in instructions, construct step-by-step plans, correctly identify necessary resources, execute those plans without getting sidetracked, and adapt to unexpected events, all while maintaining accuracy and avoiding hallucinations.
However, developing agents and testing these behaviors is no small feat: if you have ever tried to debug your own agent, you’ve probably observed how tedious and frustrating this can be. Existing evaluation environments are tightly coupled with the tasks they evaluate, lack real-world flexibility, and do not reflect the messy reality of
However, developing agents and testing these behaviors is no small feat: if you have ever tried to debug your own agent, you’ve probably observed how tedious and frustrating this can be. Existing evaluation environments are tightly coupled with the tasks they evaluate, lack real-world flexibility, and do not reflect the messy reality of