WorkBench Revisited: Workplace Agents Two Years On

[2606.13715] WorkBench Revisited: Workplace Agents Two Years On

[Submitted on 10 Jun 2026]

Title:WorkBench Revisited: Workplace Agents Two Years On

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Abstract:The best agent on WorkBench in March 2024, GPT-4, completed 43% of tasks and took an unintended harmful action, such as emailing the wrong person, on 26% of them. We re-visit the benchmark in June 2026 and find that the best agent to date, Claude Opus 4.8, completes 89% and takes an unintended harmful action on 2.5%. Aside from this considerable progress in frontier agent performance, three things stand out. First, capability and safety go together on WorkBench rather than trade off, so the models that finish the most tasks also do the least unintended damage. Second, while several classes of error have been totally eliminated, frontier models still make some basic mistakes that occasionally result in irreversible harm, such as sending an email to the wrong person. Third, the rise of open-weight models has drastically lowered costs for a performance level that was previously only accessible to proprietary models, while frontier costs have stayed relatively stable. We release an updated version of the benchmark with data and code quality improvements, new model scores, and analysis of agent progress on WorkBench since 2024.

Comments: 8 pages, 3 figures. Follow-up to arXiv:2405.00823

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)

Cite as: arXiv:2606.13715 [cs.AI]

(or arXiv:2606.13715v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.13715

arXiv-issued DOI via DataCite

Submission history

From: Olly Styles [view email] [v1] Wed, 10 Jun 2026 21:21:35 UTC (64 KB)

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