ArgaBench

ArgaBench evaluates multi-app AI agents on 40 operational tasks across IT, CRM, marketing, software development, and e-commerce, emphasizing correct final business states, safety boundaries, cross-system consistency, and repeatable performance rather than prescribed API-call sequences. Across 3,840 trials involving 32 model-and-effort configurations, 42.8% passed, 41.8% failed, and 15.4% caused unsafe actions such as unauthorized creates, edits, or deletions; Opus 5 at maximum effort led published configurations with a 70.8% pass rate. Higher reasoning effort did not reliably improve outcomes, and agents frequently produced partial work, missed required deliverables such as human-review email drafts, changed incorrect or protected records, failed to synchronize information across applications, or claimed completion without evidence in final system state. Marketing had the highest aggregate success rate, while developer tasks carried the highest unsafe-mutation rate, and CRM tasks were the least successfully completed due to identity resolution, state synchronization, and approval requirements. Repeated trials also revealed substantial inconsistency even under identical deterministic conditions, with leading models producing mixed results across many tasks. The benchmark uses resettable simulated application environments, executable outcome verifiers, and published traces and state changes, while noting limitations including its limited task set, mediated API interface, and some reliance on LLM-based judging.

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