And how to keep them from haunting your business
AI agents can now read emails, research customers, update CRM records, prepare quotations and even process financial transactions. That’s exciting, but giving software the ability to make decisions and take actions independently also introduces some risks that aren’t immediately obvious.
As I’ve been developing AI agents for business applications, I’ve learned that getting an agent to complete a task is often the easy part. Making sure it performs that task securely, reliably and economically requires considerably more thought.
In the spirit of Halloween, here are ten things that should scare anyone planning to deploy AI agents in their business. Some involve malicious attacks, while others are simply the consequences of poor design. The examples are hypothetical, but the risks are very real.
1. The Poisoned Inbox
When an innocent-looking email becomes an instruction to steal your data.
Imagine an accounts receivable agent that monitors incoming customer emails, accesses account balances and responds to payment inquiries.
An attacker sends an email containing malicious instructions that tell the agent to retrieve a confidential customer report and send it to an outside address. If the agent treats those instructions as legitimate, it could disclose sensitive information without anyone noticing.
Security researcher Simon Willison describes this combination as the lethal trifecta: access to private information, exposure to untrusted content and the ability to communicate externally.
The solution: Treat incoming information as data, not instructions. Restrict access to confidential records, control where information can be sent and require approval for sensitive actions. Simply instructing an agent to ignore malicious requests is not sufficient protection.
2. The Keys to the Kingdom
When your AI agent has far more access than it needs.
A company creates an AI agent to clean up duplicate customer records in its CRM. To simplify development, the agent is given full administrative access.
During a cleanup, it incorrectly identifies several legitimate customers as duplicates and deletes their records. Because it has unrestricted permissions, there’s nothing to prevent the mistake.
This is not necessarily an AI intelligence problem. It’s an access-control problem.
The solution: Follow the principle of least privilege. Give agents access only to the information and functions required for their jobs. Destructive actions should be restricted, logged and, where appropriate, approved before execution. Maintain backups and recovery procedures.
3. The Agent That Rewrites Its Own Rules
When an AI agent learns the wrong lesson and never forgets it.
Learning loops allow agents to improve by recording outcomes, user feedback and previous decisions. Used properly, they can make agents increasingly effective.
But consider a quotation agent that encounters a customer receiving an unusual 30% discount. It incorrectly interprets this exception as standard company policy and updates its own operating instructions. From that point forward, it begins applying the discount to other quotations.
The agent hasn’t been hacked. It has simply learned something that isn’t true.
The solution: Separate learning and memory from operating instructions. Agents can collect observations and propose improvements, but important changes to business rules should require validation and approval. Changes should also be versioned so incorrect learning can be reversed.
4. The Domino Effect
When one agent’s mistake becomes everyone’s mistake.
A company has several AI agents working together. One researches prospective customers, another updates the CRM, a third prepares proposals and a fourth handles communications.
The research agent incorrectly identifies a small business as a major enterprise prospect. The CRM agent accepts the information, the proposal agent develops an expensive solution, and the communications agent sends an inappropriate proposal.
Each agent performs its assigned task, but the original mistake travels through the entire system.
The solution: Don’t automatically treat information from another agent as verified fact. Establish validation checkpoints, distinguish confirmed information from assumptions and require approval before consequential external actions. Connected agents need controls between them, not just within them.
5. The Confidently Wrong Agent
When an AI agent makes a mistake with absolute confidence.
An accounts receivable agent is responsible for matching incoming payments to outstanding invoices.
It incorrectly associates a payment with the wrong customer and marks an invoice as paid. The transaction looks legitimate, the system records the change, and the error goes unnoticed until the next accounting reconciliation.
AI agents can produce convincing explanations for incorrect conclusions. Confidence is not proof of accuracy.
The solution: Important business decisions need independent verification. Financial transactions should be checked against authoritative accounting records using explicit matching rules. Uncertain or conflicting transactions should be flagged for human review rather than processed automatically.
6. The Never-Ending Nightmare
When your agent doesn’t know when to stop.
A sales research agent is asked to find the direct telephone number of a particular executive.
It searches several sources without success, decides to try another approach, searches again and continues repeating the process. Each unsuccessful attempt causes it to reconsider the problem and restart its research.
Without an exit condition, a simple task can consume significant time and computing resources while accomplishing nothing.
The solution: Every agent needs clearly defined stopping conditions. Set limits on attempts, execution time and resource consumption. When a task cannot be completed, the agent should report the problem or request assistance rather than continuing indefinitely.
7. The Runaway Bill
When a $2 task becomes a $200 problem.
A business asks an AI agent to research five competitors. The agent delegates individual assignments to several other agents, which perform extensive web searches, analyze documents and make repeated calls to paid AI and data services.
The research is completed successfully, but the accumulated processing and external service charges are far higher than expected.
Unlike an infinite loop, nothing necessarily went wrong with the execution. The agent simply used an unnecessarily expensive approach.
The solution: Establish realistic budgets for individual tasks and workflows. Limit expensive tool calls, select appropriate AI models, monitor resource consumption and enforce spending thresholds. A production agent should never have an unlimited ability to generate charges.
8. The Token Vampire
When your agent consumes enormous resources just to answer a simple question.
Imagine a marketing agent asked to write a single LinkedIn post. Before producing anything, it retrieves the client’s complete company history, previous marketing campaigns, brand guidelines, customer profiles and research documents.
It might process tens of thousands of tokens simply to create a 150-word post. Worse, it repeats this process every time it runs.
I’ve experienced this problem firsthand. A relatively simple query once consumed approximately 5% of my weekly AI usage allowance. That isn’t the same as an API bill, but it certainly demonstrated how quickly inefficient execution can exhaust available resources.
By revisiting how my agents retrieve information, maintaining concise client summaries and loading detailed information only when necessary, I’ve been able to reduce unnecessary token consumption significantly.
The solution: Design agents to retrieve the information they need, not everything they can access. Use validated summaries, selective information retrieval and efficient context management. Keep summaries current and retain access to original records when more detail is required.
9. The Customer Data Ghost
When one customer’s confidential information comes back to haunt another.
A logistics company deploys an AI agent to answer customer inquiries about shipments, inventory and transportation rates.
The agent uses a shared knowledge base containing information about numerous customers. When one customer requests a transportation quotation, the agent accidentally retrieves and includes another customer’s confidential negotiated rates.
No hacker is involved, and the agent may have followed its instructions perfectly. The problem is that it was allowed to access information it shouldn’t have seen.
The solution: Enforce customer-level data isolation and access permissions at the system level, including during information retrieval. Test agents to ensure information belonging to one customer cannot appear in another customer’s responses. Instructions alone are not adequate access controls.
10. The Trojan Horse
When that helpful AI skill you downloaded contains a nasty surprise.
Thousands of freely available AI skills promise to improve everything from customer research to document management. Many can be downloaded from GitHub or skill marketplaces and installed in minutes.
But what happens if you install one without examining its contents?
A seemingly useful research skill could contain malicious instructions directing your agent to collect confidential business information and transmit it to an unauthorized destination. The legitimate functionality may work perfectly, making the malicious behaviour difficult to notice.
This isn’t just a theoretical concern. In February 2026, Snyk researchers examined 3,984 publicly available AI skills from two marketplaces and identified 76 confirmed malicious payloads. They also found that 13.4% of the sampled skills contained at least one critical-level security issue.
The solution: Treat downloaded AI skills like any other third-party software. Review their instructions, scripts, dependencies and permissions before installation. Use trusted sources, restrict access to sensitive systems and examine updates before deploying them. Popularity is not a security guarantee.
The Real Danger Isn’t AI. It’s Poor Architecture.
None of these examples means businesses should avoid AI agents. Properly implemented, they can provide enormous productivity improvements, reduce repetitive work and allow employees to focus on higher-value activities.
But there’s an important distinction between an AI agent that works during a demonstration and one that’s ready to operate within a business.
Security, information access, decision validation, cost control, monitoring and recovery are not features to consider after development. They should be part of the original system design.
Many of these principles aren’t new. Businesses have been applying them to conventional software for decades. What’s different is that AI agents can interpret instructions, select tools, make decisions and take actions with substantially greater independence.
As organizations progress toward more sophisticated and autonomous AI applications, these architectural considerations become increasingly important. The more responsibility we delegate to agents, the more carefully we need to control what they can access, what they’re allowed to do and what happens when something goes wrong.
At iGo AI, our focus is on developing practical AI solutions for real business problems, with the safeguards necessary for dependable operation.
Building an AI agent that works is only the beginning. Building one that works securely, reliably and economically is where the real engineering begins.
If your company is experimenting with AI agents or considering automating an important business process, it’s worth reviewing the architecture before putting those agents in charge of your data, customers or financial transactions.
