What should a benchmark of agent direction do when the assignment itself is broken?

Reddit r/AI_Agents News
ai-agents benchmarking evaluation agent-alignment clarification hyperhat

Summary

HyperHat's founder shares a design sketch for benchmarking whether AI agents can identify contradictory task requirements and ask clarifying questions rather than blindly executing, pairing broken tasks with satisfiable controls to prevent trivial gaming of the eval.

Full transparency up front: I am the founder of HyperHat, and this post comes out of what we are building. No link in here. When we posted our first benchmark results here, the pushback that stuck with me was about judgment, not execution. The example: two requirements that cannot both hold, or a change that contradicts behavior the task says to preserve. The right move there is to stop and question the task. The design sketch that came out of the thread: the person has to identify the actual conflicting requirements and ask which takes priority; stopping without identifying the conflict earns less credit. Pair the contradictory task with a satisfiable control task, so questioning every assignment does not become the easy way to score. Then check that the clarification actually carries through into the implementation. The open question I keep coming back to: how do you score the quality of the question itself, not just that one was asked. If you have seen an eval of agent direction that tests this well, or you think the control task idea breaks somewhere, I would like to compare notes.
Original Article

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