Operations | Monitoring | ITSM | DevOps | Cloud

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).

AI spending continues to accelerate. In 2025, experts project that companies will collectively spend about $644 billion on generative AI alone — a whopping 76.4% increase from 2024. This puts it a mere $79 billion behind the public cloud as a whole, signaling the most seismic interval of new infrastructure investment since the dawn of the public cloud.

Just when you’d finally wrapped your head around AI, here comes its autonomous cousin, Agentic AI. Think of it as AI that doesn’t just assist, but acts. It makes decisions, handles tasks, and communicates with other systems on its own. While it’s revolutionizing supply chains and customer experiences, there’s a catch. These autonomous agents rely on a plethora of third-party services, and when one fails, everything stops.

With all the rapid advancements in machine learning and AI, it can feel like we’re constantly playing catch-up. Over the last two Civo Navigate conferences, Berlin 2024 and San Francisco 2025, Civo brought together leading experts to discuss the future of AI, machine learning, and the growing challenges and opportunities for developers and businesses.