TL;DR for operators Closed-loop self-training should not be treated as productive merely because the system is still generating examples and updating weights. Wang et al. propose measuring whether each new round contains patterns that were not already predictable from the previous round, then using that signal to decide both which samples deserve gradient weight and whether another round is worth running at all.1 ...
Mistakes are cheap until an agent repeats them. A human worker who keeps failing at the same task usually leaves traces: a blocked aisle, a missing tool, a wrong form field, an error message, a process exception. A competent manager does not simply tell the worker to “try again with more confidence.” The useful move is more boring and more valuable: identify the pattern, write the repair rule, and make sure the next attempt starts from the point of failure rather than from the beginning. ...
Workflow is the unglamorous part of agentic AI. Which is precisely why it matters. A research agent can have a strong language model, a decent search tool, and an impressive ability to produce paragraphs that sound like a McKinsey intern who drank too much espresso. Yet when the task becomes long, ambiguous, and evidence-heavy, the same agent often fails for a boring reason: it does the right actions in the wrong order, repeats the same weak search, summarizes too early, forgets to verify a source, or changes its own instructions so enthusiastically that it becomes a different employee halfway through the job. ...