They said that the benchmark contains 310 work environments across 52 professional domains including coding, crystallography, genealogy and music sheet notation. Each environment consists of real documents totaling around 15K tokens in length, and five to 10 complex editing tasks that a user might ask an LLM to perform.
And, they stated in the paper’s abstract: “Our analysis shows that current LLMs are unreliable delegates: they introduce sparse but severe errors that silently corrupt documents, compounding over long interaction.”
Those mistakes are significant, they said. “The findings show that current LLMs introduce substantial errors when editing work documents, with frontier models (Gemini 3.1 Pro, Claude 4.6 Opus, and GPT 5.4) losing an average 25% of document content over 20 delegated interactions, and an average degradation across all models of 50%.”
Benchmark exercise receives a thumbs up
Brian Jackson, principal research director at Info-Tech Research Group, found the findings very interesting. “Putting a list of LLMs to the test across different work domains yields a lot of useful insights,” he said. “I think this type of benchmark exercise could be helpful to enterprise developers who are looking to leverage agentic AI to automate specific workflows and understand the limits of what can be achieved.”