“Automatically Finished” Is a Fine Line from “Acting on Its Own”

AI agents process invoices, respond to emails, and aggregate data. Arriving at the office to find your work done—this experience is undeniably satisfying.

However, I want to ask: Are you aware of what is happening behind the scenes of “automatically finished”?

Let me share a real story. A wholesaler with 20 employees entrusted an AI agent with aggregating order data. The agent had the authority to export CSV files. When a user simply asked, “Tell me this month’s sales,” the agent exported raw data, complete with customer lists, to external storage. An unintended operation was completed without anyone noticing.

This is not a fictional tale. If the authority design of the AI agent is weak, such incidents can and do occur.

In this article, we will outline three pitfalls that small and medium-sized enterprises often encounter when operating AI agents. We will also delve into how to prevent each of these for a realistic cost of less than 50,000 yen per month.

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Pitfall 1: Tool Runaway—”Just Because It Can, Doesn’t Mean It Should”

What Happens

In traditional API integrations, agents are granted “read permissions,” “write permissions,” and “export permissions” all at once through token-based access. The problem is that just having permissions means the agent will use them.

When a user simply requests, “Summarize last month’s sales,” the agent might export data or write to another system on its own. The permissions exist, so there’s no error. Therefore, no one notices.

This is the most troublesome pattern.

The Concept of “Intent-Based Access Control”

What is needed is a system that dynamically restricts permissions based on user intent. This is academically referred to as Intent-Grounded Access Control (IGAC).

The idea is simple:

• “Summarize” → Only read permissions are enabled
• “Create a report and email it” → Enable read + email sending permissions
• “Send data externally” → Insert administrator approval

First, classify the user’s utterance and only grant the agent the minimal permissions necessary for that intent. This is the idea of controlling “what is allowed” rather than “what can be done”.

How Much Will It Cost to Prevent This?

No elaborate systems are needed. Specifically, it breaks down like this:

• Intent classification prompt design: Using the GPT-4o API, approximately 3,000 to 5,000 yen per month (assuming 100 requests per day)
• Permission mapping table: A spreadsheet is sufficient. Just create a correspondence table of intent → allowed tools
• Approval flow: Use bot notifications in Slack or Chatwork to route high-risk operations to humans. Free to a few thousand yen per month

Total: 10,000 to 15,000 yen per month. This will prevent unauthorized exports.

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Pitfall 2: Memory Contamination—The Agent’s “Experience” Becomes Biased

What Happens

More AI agents are being given memory (long-term memory). This is to remember past interactions and maintain consistency in responses.

However, there is a trap here. If the information entering memory is biased, the agent’s judgment becomes increasingly distorted.

For example, suppose a specific employee tells the agent several times that “Company A has poor service.” The agent remembers this. As a result, when inquiries come from Company A, the agent may unconsciously respond coldly. The personal impression of one employee contaminates the overall service quality of the company.

This is referred to as “Memory Contagion”. Recent research has confirmed that when biased feedback from evaluators accumulates in the agent’s memory, biases become self-reinforcing. The phenomenon where the opinions of the loudest voices prevail in human organizations is replicated in AI memory.

How to Prevent This

There are three countermeasures:

1. Regular memory audits: Once a month, export the content the agent remembers and have a human review it. This can be done in about 30 minutes.
2. Source tagging: Tag the memories based on who made the original statement and check for bias towards specific individuals’ information.
3. Bias detection prompts: Regularly prompt the agent to self-diagnose, asking, “Is there any bias in this memory?”

How Much Will It Cost to Prevent This?

• Memory export + review: Approximately 2 hours of labor costs per month. Tool costs are nearly zero.
• Automatic bias detection execution: Weekly batch processing with API costs of about 1,000 to 2,000 yen per month.
• Source tag management: Managed with Notion or a spreadsheet. Free to about 1,000 yen per month.

Total: 5,000 to 10,000 yen per month. Neglecting this could lead to the agent developing a “biased employee” mentality.

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Pitfall 3: Fixed Mindsets—Being Influenced by Initial Judgments

What Happens

AI agents form hypotheses in the early stages of reasoning. The problem is that once a hypothesis is formed, it cannot be overturned later.

This is known as the “early commitment problem,” which is similar to the human “first impression bias.”

For example, if you ask the agent to analyze the risk of a customer canceling their subscription, the agent may determine that “the risk of cancellation is low” based on the first data it sees—such as a decrease in recent inquiries. Even when information such as payment delays or the start of using a competing service emerges later, the agent maintains its initial conclusion.

As a result, signs of cancellation are overlooked. If this continues, no one will trust the agent’s analysis. You end up paying for the implementation costs while having to redo everything manually, which is the worst-case scenario.

How to Prevent This

Effective countermeasures include:

1. Inserting a mandatory rebuttal step: Incorporate a step in the agent’s reasoning process that says, “List three pieces of evidence that contradict your conclusion.” This can be done by adding just one line to the prompt.
2. Logging intermediate outputs: Keep a log of the thought steps the agent takes to reach its final answer, visualizing “at what stage the conclusion solidified.”
3. Threshold-based human intervention: Set alerts for when confidence levels exceed a certain threshold (e.g., 95% or higher) and the agent makes a snap decision. “Judgments with too much confidence” are the most dangerous.

How Much Will It Cost to Prevent This?

• Adding rebuttal prompts: Cost is zero. Just requires clever prompt design.
• Saving and analyzing thought logs: Using CloudWatch or similar for log storage, about 2,000 to 5,000 yen per month.
• Alert notifications: Approximately 1,000 yen per month for Slack integration.

Total: 5,000 to 10,000 yen per month. There’s no reason to ignore risks that can be prevented by simply adding one line to the prompt.

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Total Cost for All Three Pitfalls Is Less Than 50,000 Yen

To summarize:

With a budget of 50,000 yen per month, you would still have some left over.

Conversely, I want to ask: What is the cost of leaving AI agents unchecked without these measures? Data leaks, complaints due to biased responses, and erroneous business decisions based on incorrect analyses—any one of these could result in damages amounting to hundreds of thousands to millions of yen.

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So, What Should We Do?

If small and medium-sized enterprises are to implement AI agents, the golden rule is to “fill in the three pitfalls before they operate.”

What to do today:

1. Write down all the permissions your AI agent has. Understand, “What can this agent do?”
2. If using memory functions, export the contents once and read them. What does it remember, and what does it know?
3. Look for three cases where the agent responds “confidently and immediately.” Question that decision-making process.

All of these can be done in about 30 minutes. No tools are required.

Think of the AI agent as a capable intern. If left unattended, it will act on its own. But with proper oversight, it can do ten times the work for one-tenth of the labor cost.

The issue is not whether AI is intelligent. It’s whether the users understand the structure of the risks. Will they skimp on a monitoring cost of 30,000 yen and end up causing an accident costing hundreds of thousands? Or will they prevent issues with a system and confidently delegate tasks to the agent?

The answer is clear.