When it comes to AI agents, the failures that occur are often not the ones we expect. Rather than theoretical flaws, it’s the practical, everyday breakdowns that can be the most damaging. One common issue is context bleed, where an agent carries over memory from a previous task into the next one, causing outputs to drift and become confidently wrong by the sixth step of a ten-step process.

Another problem is the tendency of agents to fill gaps in their knowledge with invented details, rather than saying ‘I don’t know.’ This can lead to personalized messages that reference non-existent information, which can be costly when dealing with clients. The resulting human review queue can quickly become overwhelming, with a backlog of items waiting to be reviewed and the entire pipeline grinding to a halt.

These failures are not due to flaws in the AI models themselves, but rather to systems problems that arise from the way the agents are integrated into workflows. The AI component is often the least broken part of the system, with the majority of issues stemming from the lack of notification systems, poorly designed pipelines, and inadequate review processes.

So, what other unseen failures have you encountered in your experience with AI agents? Share your stories and help shed light on the often-overlooked breakdowns that can occur in these systems.

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