Developer team built a double-entry accounting system to cap AI agent spending

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A software team running ten simultaneous AI agent sessions discovered their autonomous fleet exceeded a $100 spending cap almost immediately after it was set, with accrued costs already at $156 before the limit took effect. To gain financial visibility, they redesigned their logging system using double-entry bookkeeping via hledger, tracking money, promises, and labor as separate commodities across three journals. Each agent action — task opened, task taken, task completed — was mapped to ledger entries, turning scrolling logs into queryable financial obligations. An automated throttle called mesh-pace halts new task dispatch when rolling five-hour spending breaches the cap, and resumes automatically once older spend ages out of the window. The system is designed to fail open rather than fail closed, ensuring a broken monitoring tool does not paralyze the entire agent fleet.

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