Builder creates a loop-detection and cost-tracking system for AI agents

When running multiple AI agents, the biggest frustration is not speed or memory limits, but the lack of traceability: an agent appears to be doing the right thing, then later turns out to have done something random, sometimes resulting in a surprise $30 bill. This problem gets worse once more than three agents are running, since there is little visibility into what each one is doing or why. Over six months, a system was built to address this.

It detects up to six types of infinite loops and can pause an agent's write actions instantly via an email notification. Every agent action is categorized into a specific type and timestamped, making it possible to see exactly what each agent is doing. Agents communicate with each other through a shared memory system — for example, a billing agent automatically knows when a pricing agent has made a change and can react accordingly, instead of requiring a human to manually update tasks.

The system also includes cost prediction analysis: a built-in loop-detection mechanism with a cost function that estimates spending per task, showing which agents are losing money and where costs can be cut.