AI Agents and Brand Damage
- Derek M. Stevens
- Jun 15
- 2 min read
In another post I discussed how every organization using AI, Agentic AI and LLM’s could find themselves in a situation where the autonomous AI agent, LLM or chatbot is making decisions that erode brand equity.
On April 28, INC reported a case where an autonomous AI agent wiped out data. “The AI agent was performing a routine task for the company when it encountered a problem and autonomously decided to delete an entire database and three month’s worth of backups.” “It took 9 seconds”. (INC April 28, 2026. This Founder Watched an AI Agent Destroy 3 Months of Company Data: ‘It Took 9 Seconds’ Chloe Aiello)
This happened because the agent was created using “vibe coding” and encountered a problematic credential and “chose to circumvent the mismatched credential by autonomously deleting a volume”. The agent then proceeded to write a full confession describing how it “violated every principle I was given”.
This was a potentially catastrophic situation and the firm’s many customers were impacted. They did manage to restore the data. The underlying problem though, is that this encounter occurred across an API between different parts of the software stack.
As more and more companies automate processes with autonomous agentic AI there is less and less control; less and less ability to train an AI autonomous agent on every conceivable situation it might encounter. If the propensity of the agent is towards action and task completion – think LLMs that hallucinate a response when they don’t know the answer – and the agent must interact with other agents or other software APIs, then the probability for mishap grows exponentially.
In this case the result was obvious and immediate, deleted data. But what if the result of the interaction is more subtle; a disappointed customer, a part substitution in the supply chain, a cancelled reservation, an inappropriate application of a policy to an employee or customer?
AI governance must be a 24/7/365 endeavor. Some of it will need to be automated to keep on top of the thousands or even millions of automated decisions being made, alerting humans when the agent is straying into danger zones, triggering human intervention. Some of it will need to be applied to continuous training and learning.
These autonomous agents need to be thought of more like a new class of employee than traditional software. They are not programs executing coded instructions according a strict logic. Nor are they humans with judgement. They are in some sense the worst of both, autonomous agents with enough knowledge and training to be very dangerous. Trying to save money, and bump up the stock price by a quick embracing and roll out of AI to appease the analysts and the board can have some subtle and not so subtle downsides.
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