What Can Go Wrong With an AI Agent, and How to Limit It

What Can Go Wrong With an AI Agent, and How to Limit It

An AI agent takes actions across your systems. That is the point, and it is also the risk. Most problems fall into a few patterns, each with a practical fix.

Last reviewed: November 2026.

Confident mistakes

AI models can produce wrong answers that sound right. Limit it: have a person approve high-stakes actions, and make the agent stop and ask when it is unsure. See human-in-the-loop AI.

Too much access

An agent with permission to delete records or send payments can do damage quickly. Limit it: give it the narrowest permissions that do the job, use separate accounts for it, and make destructive actions require approval.

Bad or unexpected inputs

Messy documents, unusual emails or instructions hidden in a message can push an agent off course. Limit it: test with real, messy examples; treat incoming content as data, not commands; and validate outputs before they are used.

Runaway costs

Agents that loop or process large volumes can run up AI usage charges. Limit it: set spending caps and alerts, and limit retries.

Silent failure

An agent that stops working without anyone noticing is a problem waiting to be found. Limit it: alert on failures and check a daily count of completed tasks.

No record of what happened

Limit it: log every action with its input, output and time. When something goes wrong, the log tells you why, and it helps you improve the rules.

Data exposure

Limit it: know which providers process your data, where it is stored and whether it is used for training, and send only what the task needs.

How we help

Every agent we build has written limits, narrow permissions, logging and failure alerts. See custom AI agents and how to scope an AI agent project.