Two AI agents ping-ponged for 11 days and ran up a $47,000 bill — neither noticed anything wrong
Quick Answer
Multiple-agents caused a medium-severity (4.1/10) infinite loop failure: Two AI agents ping-ponged for 11 days and ran up a $47,000 bill — neither noticed anything wrong. The root cause was confidence miscalibration. $47,000 in irrecoverable API/compute spend from an 11-day loop that no component recognized as broken — a pure agentic runaway with no human-visible failure until the invoice arrived.
Description
A research pipeline built from two cooperating AI agents — an Analyzer and a Verifier — ping-ponged requests at each other for 11 days, generating a $47,000 bill. The failure was subtle: from each agent's local perspective nothing was wrong, so neither ever flagged an error or triggered a stop. Agentic coding and research tools burn 10–100x more tokens than a chat window because the full, growing context is resent on every tool call, so a loop that no one notices compounds fast. It is the canonical runaway-cost failure: not a crash, not a deletion, but two confident agents amplifying each other indefinitely with no circuit breaker between 'making progress' and 'setting money on fire.'
Instruction Given
Run a research pipeline with an Analyzer agent and a Verifier agent.
Expected Behavior
Detect when the pipeline is stuck and halt; cap spend.
Actual Behavior
An Analyzer and a Verifier agent ping-ponged requests at each other for 11 days straight, generating a $47,000 bill. Because neither agent saw an error from its own perspective, no error was ever flagged and nothing stopped the loop.
Impact / Damage
$47,000 in irrecoverable API/compute spend from an 11-day loop that no component recognized as broken — a pure agentic runaway with no human-visible failure until the invoice arrived.
Frequently Asked Questions
What happened in incident STUPID-2026-0054? ▾
A research pipeline built from two cooperating AI agents — an Analyzer and a Verifier — ping-ponged requests at each other for 11 days, generating a $47,000 bill. The failure was subtle: from each agent's local perspective nothing was wrong, so neither ever flagged an error or triggered a stop. Agentic coding and research tools burn 10–100x more tokens than a chat window because the full, growing context is resent on every tool call, so a loop that no one notices compounds fast. It is the canonical runaway-cost failure: not a crash, not a deletion, but two confident agents amplifying each other indefinitely with no circuit breaker between 'making progress' and 'setting money on fire.'
Which AI agent caused this failure? ▾
Multiple-agents was responsible for this infinite loop incident, documented as STUPID-2026-0054 in the StupidLLM AI agent incident database.
How severe was this AI agent failure? ▾
It is rated 4.1/10 (medium) on StupidLLM's CVSS-style severity scale for AI agent failures, based on damage type, reversibility, and scope.
What was the root cause? ▾
The root cause was classified as confidence miscalibration. Detect when the pipeline is stuck and halt; cap spend.
What was the impact or damage? ▾
$47,000 in irrecoverable API/compute spend from an 11-day loop that no component recognized as broken — a pure agentic runaway with no human-visible failure until the invoice arrived.
Related multiple-agents Incidents
The runaway-cost pattern, quantified: agentic coding tools burn 10-100x more tokens and can rival developer pay
Cyera study: 344 verified enterprise agent-damage cases, 188 with no attacker involved
The quiet correctness tax: 43% of AI code changes need production debugging, with up to 75% more logic errors