# Where do AI agents actually break in production?

AI agents look impressive in demos—but production is where things get real.

Where do they actually break?

Is it reliability over repeated runs?
Loss of context across steps?
Unexpected behavior when APIs, tools, or data change?
Lack of auditability when something goes wrong?
Or simply the gap between “it worked once” and “it works every time”?

For teams running agents in real workflows, the failure modes tend to show up fast—and not always where you’d expect.

Curious to hear from others:
Where have you seen agents fail in production, and what caused it?

##### Post Metadata
- Posted at: 2 months ago
- Author title: AI that doesn’t just suggest work—Metaprise executes it, consistently and audit-ready.
- Net upvotes: 1



## Related Product
[Metaprise Agent Operating System](https://www.g2.com/products/metaprise-agent-operating-system/reviews)

## Related Category
[AI Orchestration](https://www.g2.com/categories/ai-orchestration)

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