Operations | Monitoring | ITSM | DevOps | Cloud

The hidden reliability risks in your agentic AI workflows

Artificial intelligence recently took a major leap from “saying” to “doing.” Instead of simple back-and-forth chats, we’re now allowing automated AI processes to take action on our behalf—from responding to emails to building and deploying complete applications. This shift from “assistant” to “actor” can make applications more capable, but it also creates additional failure modes.

Test your AI model training reliability, too

Training is at the heart of every LLM model, but it’s still an application running on an infrastructure, which means it can fail. Our GPU test helps you test your training GPUs so you don’t lose that valuable work. TRANSCRIPT: One of the things we built recently was the GPU Gremlin. So if you are training a bunch of models and you're doing a bunch of GPU testing. You know, we want to give you the tools to be able to go test that, to understand how training the model could fail.

How Gremlin makes disaster recovery testing easier and faster

There’s a common saying: “A backup isn’t a backup until you’ve tested it.” The same is true whether it’s a simple database failover or an entire data center/cloud provider failover. You simply won’t know if it works if you don’t test it. When it comes to disaster recovery testing, that can be an expensive, painful, and arduous process. But it’s required by companies for a reason. And not just for disasters like hurricanes, flooding, or earthquakes.