← Back to Glossary
Failure Modes in AI Agents
Definition
Failure modes in AI agents are recurring patterns in which an agent produces incorrect, unsafe, inefficient, or unintended behavior due to limitations in context, reasoning, data, tooling, or system design.
Purpose
The purpose of identifying failure modes in AI agents is to anticipate risks, design mitigation strategies, and improve system reliability by understanding how and why agent behavior can break down in real-world operation.
Key Characteristics
- Errors caused by incomplete, misleading, or outdated context
- Incorrect tool selection or improper tool usage
- Accumulation of errors across multi-step or long-running tasks
- Misalignment between agent goals and system constraints or policies
- Unbounded autonomy leading to unsafe or unintended actions
Usage in Practice
In practice, analysis of failure modes in AI agents is used to design safeguards, introduce monitoring and human oversight, improve prompt and context management, and guide architectural decisions in production agent systems.
One implementation of this concept is offered by Kenaz through the Red Teaming service.