How to Stop an AI Agent Infinite Loop
A simple planning request triggered 105 calls to the same goal tool. The system did not crash immediately. It kept spending tokens until the turn limit stopped it, without producing a useful answer. This was INC-001: goal tools were exposed globally instead of being scoped to the node that owned goal state. Every model node could ask for the same incomplete goal, so the graph kept returning to…
An unexpected issue arose when a straightforward planning request triggered 105 calls to the same goal tool. The system did not crash instantly; instead, it continued to expend tokens until the turn limit intervened, all without delivering a useful response. This incident was labeled INC-001, where goal tools had been improperly exposed globally rather than being confined to the node responsible for maintaining goal state.
As a result, every model node could request the same incomplete goal, causing the graph to continuously loop back to the same decision point.
To mitigate such problems, it was crucial to assign an explicit owning node to each tool. Additionally, implementing maximum turn counts and per-tool call limits could prevent excessive resource consumption. Another key measure involved detecting transitions where no progress was made, allowing for timely intervention. It was also recommended to rigorously test the live plan-to-execute route using small requests to ensure the system could handle typical scenarios without falling into an infinite loop.
While configuration values serve as safeguards, they are only effective if the system actively reads and enforces them during runtime. The final step was to conduct thorough regression testing. This included asserting that specific requests, such as a 4+4 calculation, should terminate correctly, and ensuring that planning nodes were unable to call goal-state tools directly.
Furthermore, utilizing a prevention scanner through the prevention-scanner tool, with a specified format for output, could help identify source patterns associated with this incident. Pairing this scanner with graph analysis, load testing, and termination tests would provide a comprehensive approach to preventing similar issues in the future.
It was emphasized that the scanner, while heuristic, should be used in conjunction with other testing methods for optimal results.
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