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Creating an agentic feedback loop with reliability guardrails

Reliability guardrails help make sure that your applications stay reliable without slowing down. In an earlier blog, we went into why agentic AI development needs reliability guardrails. It went over how the increased speed of AI development demands automated guardrails to verify resilience and what kinds of tests these guardrails should cover. But that’s only the beginning. By themselves, guardrails act as a gate to ensure resilience mechanisms hold under rapid changes.

Why agentic AI development needs reliability guardrails

AI has massively accelerated code deployment. In fact, since the introduction of agentic coding, GitHub has seen exponential growth in PRs, commits, and new repos. What they originally predicted would require 10X capacity, they’re now estimating it’s going to require 30X capacity, and the biggest driver is agentic development. Companies across industries are building agentic pipelines to ship features faster than ever before. That acceleration isn’t without risk.

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.

Disaster Recovery Testing by Gremlin

Do you know how your system will respond when major outages strike? Disaster Recovery Testing safely simulates real catastrophic failures across your entire system. You can centrally and easily run zone, region, and datacenter-scale reliability tests across your entire organization simultaneously for disaster recovery, business continuity, compliance verification, and more. With Disaster Recovery Testing, tests that used to take engineering-months and dozens of experts can be done safely and securely in hours by a single person.