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AI agents are proliferating, often beyond IT managers' control, and the costs of unseen agentic activity can add up quickly, a recent analysis shows.
StackGen's latest State of Reliability Report found that unregulated or semi-supervised agents are proving to be problematic on many levels.
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In at least nine documented cases over the past year, AI agents destroyed live company systems by wiping data and deleting databases on their own, using valid credentials. Their efforts were invisible to standard monitoring until the damage appeared.
The study, which analyzed 109,000+ incidents, also found that as failure modes multiply, recovery isn't speeding up: median resolution times have been roughly flat since 2023, and the single most common fix is waiting for another company's engineers.
From a financial perspective, AI agent costs can rack up. Recently, engineers at Revenium, an AI spending solutions provider, reported an agentic AI session where a developer left one AI coding assistant running on his laptop for four days straight. By the time anyone noticed, the agent had made 4,819 calls at a total cost of $3,762, without anyone's knowledge.
While expenses vary considerably with use case, basic guidelines pulled together by BakedWith put the cost of a basic chatbot at $20 to $50 per month, a mid-level agentic assistant at $100 to $500 a month, and a custom enterprise agentic solution exceeding $10,000 upfront.
Still, these are upfront costs, and pricing does not account for runaway costs with under-supervised agents, which, of course, can quash ROI. This potential issue outlines how organizations must manage the AI agents they deploy and underscores the risks of their unchecked proliferation.
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Revenium cited an example from one of its customers, a mid-sized e-commerce company, which "saw AI agent infrastructure costs jump from $5,000 per month during prototyping to $50,000 per month in staging -- a 10x increase driven by unoptimized RAG queries and recursive agent loops during high-volume periods."
Every individual agent action looked like good engineering: "Each action was rational in isolation, but the cumulative cost was not."
In another case, a team's AI agents "entered an infinite conversation loop that ran undetected for 11 days, burning through $47,000 before anyone noticed," the report added.
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"Two agents got stuck talking to each other while the team slept, while they worked, while they believed the system was just running smoothly."
In a recent internal study, Revenium's engineering team turned attention to its own agentic AI practices and also found costly activity.
Here's what the team learned from a particular unmonitored AI agent incident and related audit:
While these numbers represent just one engineering organization, the Revenium team felt "the shape of the distribution holds broadly, and it tracks with patterns we see in early customer deployments."
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At the same time, "lower cost per pull request does not mean better engineering. We're not suggesting anyone replace their higher-cost engineers. The point is about measurement: the variance is currently invisible to the budget owner, and the invisibility is the problem."
Source: ZDNET
