Understanding the New Risks of AI Agents: A Practical Guide for Organizations

AI agents are emerging as one of the most promising applications of large language models. Unlike traditional chatbots or copilots, agents are designed to operate autonomously within digital environments. They can decide, act, and interact with systems on behalf of users. This unlocks powerful use cases, but also introduces new categories of risks that organizations must address.

In this article, we focus on defining these risks clearly and practically, a necessary first step before designing any safety solutions.

From Output Risks to Action Risks

The key shift brought by AI agents is the move from output risks to action risks.

Traditional LLM risks such as hallucinations, offensive content, and bias concern the content generated in response to a prompt. Agent risks go beyond that, because agents don’t just generate responses, they autonomously perform tasks, interact with APIs, trigger workflows, and manipulate data. This means that failures or misbehaviors can have operational, financial, or security consequences, not just reputational ones.

A Practical Taxonomy of AI Agent Risks

At Alinia, we use a pragmatic risk classification that helps organizations think concretely about where risks originate.

Agent-related Risks

These risks relate to the inner functioning of the agents, and to those derived from their autonomous interaction with the external environment. More specifically:

• Errors in Reasoning and Planning – Agents are often tasked with multi-step goals that require planning and execution. Errors in reasoning — such as hallucinating incorrect information — can propagate across multiple actions, leading to cascading failures.

  For example, a banking agent instructed to «optimize my investment portfolio» might generate a flawed strategy based on inaccurate assumptions, resulting in poor financial decisions.

• Vulnerability to External Manipulation – Agents continuously interact with their environment. This exposes them to Prompt Injection Attacks — scenarios where a malicious user or external content manipulates the agent’s behavior.

Example: A financial advisor agent scraping web content for market insights could be fed misleading data embedded in websites, altering its investment recommendations.

• Data Privacy and Exposure Risks – Agents may inadvertently disclose sensitive data — either through reasoning errors or external attacks.

For example, an agent booking travel may transmit sensitive user details (passport, payment info) to third-party APIs with inadequate security.

• Irreversible Harmful Actions – Unlike content generation errors, the actions performed by an agent can cause permanent damage.

For example, a banking agent that mistakenly wires money to the wrong account creates a potentially irreversible financial loss.

User-related Risks

These risks arise from the interaction between the user and the agent, especially from unclear or malicious user inputs.

• Unclear or Incomplete Instructions: A user may give instructions that lack sufficient detail, causing the agent to take actions that don’t align with their expectations.

Example: A customer tells their banking agent: « Move money from my savings to my main account for rent and bills.» If the agent misinterprets this vague instruction, it might transfer too much or too little money.

• Exploitation by Malicious Users (Prompt Injection/Jailbreaking: Malicious users may try to exploit the agent’s vulnerabilities by injecting malicious commands or bypassing safety features. Example: A malicious user may attempt to trick a personal finance assistant into transferring money by embedding harmful code within the data inputs (e.g., embedded in a user’s transaction history or web searches).

Why These Risks Matter Now

AI agents are moving from prototypes to real-world deployments across industries, including high-stakes domains like personal banking:

• Automating routine financial transactions
• Acting as personal finance advisors
• Monitoring spending and suggesting optimizations
• Managing bill payments or savings plans

As this happens, organizations must recognize that the risk surface expands dramatically compared to traditional LLM-based systems.

Safety becomes not just a matter of content filtering, but of controlling decisions, actions, and system interactions.

Moving from Problem Definition to Solutions

In our next blog post, we will explore how organizations can practically address these risks.

This includes designing safety frameworks specifically adapted to agents, monitoring and controlling their behavior at runtime, and implementing guardrails that go beyond prompt engineering or output moderation.

At Alinia, we are developing these solutions, based on the core idea that safety goes beyond merely preventing failure, and it’s about fostering trust, ensuring control, and supporting meaningful human oversight in every interaction.