Ninety Days of Agent Work

Between April 10 and July 10, 2026, engineers recorded 3,153 sessions with coding agents, generating 251,125 messages, primarily from the Claude Code tool, to study agent interaction and performance. This dataset, which largely consists of tool-generated text, was used to derive a 93% tool output metric, indicating that most of the content originates from the system rather than user prompts. During these sessions, engineers frequently intervened to correct the agents, with corrections occurring in 1,554 out of 2,007 labeled sessions, highlighting the importance of user feedback in improving agent skills. The study also identified a significant security finding, with 10% of sessions inadvertently leaking credentials, underscoring the necessity for careful data handling. The sessions revealed a 60% full resolution rate of tasks, while 28% were partially completed, providing valuable insights into agent performance and training potential. This comprehensive dataset, stored in a queryable format, offers significant potential for refining agent models and improving security practices within the engineering field.

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