agent_reliability
The 7 ways an AI agent fails in production
A convincing demo and a dependable agent are different systems. The gap is a short list of loop-engineering disciplines that decide whether an agent survives real traffic. Here is each failure mode, the incident it causes, and the fix, or score your own loop with the free scorecard.
The agent that never stops
Without a hard stop, an agent handed an ambiguous or impossible task keeps calling tools and burning tokens long after a person would have given up, because nothing in the loop tells it to quit.
The failure and the fixEscalation & failure handlingThe confident wrong answer
When an agent hits its limit or can't make progress, the dangerous outcome is not a crash, it's returning a half-finished answer reported as success, so a failure reaches the user looking like a result.
The failure and the fixTool-output integrityThe tool result that hijacks the agent
Tool results, web pages, emails, and API responses get fed back into the model's context, and if they're appended raw, an instruction hidden in that content can redirect the agent, because the model doesn't reliably separate data from commands.
The failure and the fixIdempotency & side effectsThe action that fires twice
If the loop retries a step that already had a side effect, sending an email, charging a card, the action can fire again, because a retried tool call has no memory that the first one succeeded.
The failure and the fixContext managementThe agent that forgets the goal
On a long task, as the context window fills and older turns get truncated, the agent can drift from or forget its original goal, which looks like the model quietly getting worse for no reason.
The failure and the fixCost & rate controlThe runaway invoice
Without a per-run ceiling, a single agent run can quietly rack up a large token and dollar bill, because nothing stops a loop that keeps calling expensive models and tools until it happens to finish.
The failure and the fixObservability & evalsThe agent you can't debug
When a user says the agent did the wrong thing and the original run wasn't traced, you're left re-running and guessing, because there's no record of the prompts, tool calls, and results that produced the behavior.
The failure and the fix