Engineering Resilience in AI-Integrated Systems: What Happens When Your LLM Dependency Fails?
Your application is healthy. The database is responding. The dashboard is green. A user asks the AI assistant a question, and nothing comes back.
This is an awkward kind of outage because the rest of the system may be working perfectly. The model service could be slow, rate limited, unavailable, or returning an answer that sounds convincing but is wrong. From the user's perspective, the feature has failed either way.
I work on reliability for systems that support inventory and fulfillment operations. In one AI-assisted investigation project, the aim was to help users explore discrepancies through natural language instead of relying on an engineer to write every query. That kind of interface can make complex data much easier to use. It also adds a dependency between the person asking a question and the evidence they need to see.
So the resilience question is bigger than "What if the model goes...
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