Runaway AI agent opened 95 browser tabs, prompting devs to build open-source watchdog
A Claude AI agent tasked with distribution research entered a runaway loop, opening 95 browser tabs and consuming enough resources to bring down every other agent running on the same system. No errors were triggered during the incident, highlighting a silent failure mode common in unattended AI agents. A post-incident review of 61 Claude Code session transcripts revealed three distinct warning patterns: runaway tool-call loops, abnormal token burn rates, and repetitive call bursts. In response, the team built BurnGuard, a stdlib-only Python tool that monitors agent sessions using a sliding 10-minute window and call-signature analysis to distinguish genuine runaways from legitimate high-activity agents. The tool is open source and can be run as a one-shot scan or a continuous watch process.
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