Operations | Monitoring | ITSM | DevOps | Cloud

I’m genuinely excited about what we're rolling out with Copilot Editor, an update to our AI that’s truly packed with new capabilities designed to help you automate pipeline development. You can read about these capabilities here. I wanted to take a moment to share our thinking on a core principle that guides how we build, especially regarding the impactful, and sometimes daunting, world of generative AI.

When you run critical IT or Security systems, you are still on the hook if an alert is missed or a pipeline drops data. That is why Cribl Copilot never tries to replace you. Instead, it works as a partner you can question, guide, and overrule.

Over the past decade, organizations have embraced automation in waves – starting with basic task scripts and Robotic Process Automation (RPA), then moving to hyperautomation, and now exploring “agentic AI” as the next frontier. Each step in this evolution has expanded the scope of what can be automated, and revealed new challenges. This blog offers a detailed comparison of RPA, hyperautomation, and agentic AI, their key differences, strategic advantages, and potential drawbacks.

Hyperparameter tuning is a critical step in optimizing large language models (LLMs). Parameters such as learning rate, batch size, weight decay, and number of training epochs can significantly affect convergence behavior and final model performance. While several approaches like grid search or random search are widely used, executing them manually is inefficient; especially when each training run is compute-intensive.

Ever spent a week wrangling log pipelines just to get your SIEM to stop screaming about missing fields? Wasted way too much time stripping out noisy events and reformatting data for analytics? You’re not the only one. If you work in Security or ITOps, you know the pain: every new data source means another round of schema headaches, more manual mapping, endless field transformations, and a quick prayer that you didn’t break something critical (or let in a flood of junk events).