Why are realistic datasets for agent workflows still so hard to find?

Working on agent systems internally and we keep running into the same issue where most public datasets/evals still feel much cleaner and more controlled than real production environments. A lot of the common datasets and benchmarks are: \- short interactions \- clean tool responses \- predictable workflows \- well-formed user inputs \- isolated tasks \- minimal state drift \- low ambiguity / low interruption scenarios which ends up being pretty different from what deployed agent systems actually face. We’ve been trying to find stronger datasets around: \- multi-step workflows with long-running state \- tool failures / partial responses \- conflicting tool outputs \- interruption-heavy user behavior \- ambiguous or underspecified requests \- retries / recovery scenarios \- long conversational drift over time \- agents operating under degraded conditions \- edge cases that only appear after extended interaction chains Any recommendations on where to find datasets like these would be appreciated. Feels like most public agent datasets still underrepresent the kinds of messy interaction patterns systems actually face once they hit production traffic.