Running many AI agents at once: the failure points that surface first

A dozen parallel agents sharing one codebase, database, and API budget create an operations problem more than a modeling one. The article expects collisions over shared files or rows, unchecked token spending from retry loops, and rate-limit errors that appear random when many agents hit them together. It also flags silent agent failures and drift when agents interpret a goal differently, and warns that reviewers can be overwhelmed as output outpaces human checking. The suggested remedies are isolated workspaces, per-agent budgets, queued and backed-off API calls, required status reports, and a single shared goal document. The author presents this as a checklist of anticipated problems rather than a measured study, and recommends scaling up only after a small setup runs reliably.