Rohan Paul

New Microsoft paper shows that agents can learn software skills from raw usage logs by keeping only skill edits that better predict users' next actions. i.e. You do not need a live test environment to check whether a new agent skill helps, because next-action accuracy on old logs tracked live success. Usage logs hold lots of know-how, but they record no goals, often mix several tasks, and cannot be replayed. Earlier methods, like Agent Workflow Memory, need goal-labeled examples or a live environment to test changes. TeleTune guesses each session's goal and has the model predict every logged action using a text skill library. Wrong guesses suggest library edits, and an edit stays only if accuracy rises on held-out logs. If your product records user activity, mine it for agent skills and judge each change by next-action accuracy on held-out logs. – arxiv. org/abs/2610.05437 Title: "TeleTune: Evolving Agent Skills From Offline Telemetry"