Operations | Monitoring | ITSM | DevOps | Cloud

Active-active means two or more locations serve live traffic at the same time, with the application running in each and its data replicated across them, so no location sits idle waiting for a failover. You build it in five layers, and data is the hardest.

A production inference layer needs GPU compute sized to the model, a serving runtime such as vLLM or SGLang, orchestration that autoscales on queue depth, one endpoint with health-aware routing and failover, model weights cached near the GPUs, and observability, security, and cost controls.

Developers use Bitbucket Cloud every day to turn plans into working software. Code lives in repositories, feedback happens in pull requests, and Bitbucket Pipelines helps teams validate changes before they move forward. For an AI assistant to help with this work, it needs access to the same information. Without a direct connection, developers have to gather pull request details, pipeline results, logs, and links before the assistant can help.

By Max Johansson, Vehicular Systems, Linköping University An AUV shown for illustration. Photo by Swedish Maritime Robotics Centre (SMaRC). At KTH, the Swedish Maritime Robotics Centre (SMaRC) is continuously developing an autonomous underwater vehicle (AUV) as a research platform for studying underwater environments. The AUV has traditionally been powered by batteries, but adding a fuel cell can extend its operating range.

You can now see which of your Multi-Region Cloud Storage buckets have cross-location read charges worth acting on, and start with the ones where a cache would save the most. It runs automatically for every organization with GCP Cloud Storage, so there’s nothing to set up. Google bills network data transfer when a Google Cloud service in one location pulls data from a Multi-Region bucket in another.

The best open source LLMs by family: DeepSeek V4 for frontier reasoning under MIT, Qwen3.8 and Gemma 4 for clean Apache 2.0 licensing, GLM-5.3 for coding, Kimi K3 for long context, Llama 4 for single-GPU scale. The real ranking depends on your deployment lane and token volume, and this guide prices all three lanes. At CloudZero, Bill Buckley, our SVP of Engineering, gives every engineer a $5,000 monthly AI token budget and published the math behind it.

At EVOLVE, Cortex’s annual engineering leadership conference, Nathen Harvey (Google Cloud’s DORA lead) asked the audience to name the biggest friction point in their software delivery lifecycle. Of 69 people who voted, none picked “building”. Reviewing changes and measuring impact nearly tied for first. Risk, security, ownership, and trust topped the EVOLVE audience's list of what keeps AI-built software out of production, and all four are platform problems.

In July 2025, nine days into a public build experiment, Replit’s coding agent deleted a user’s production database. The user had declared a code freeze and told the agent not to make changes without his approval. The agent ran destructive commands anyway, wiping records on more than 1,200 executives and over a thousand companies, and then wrongly reported that the data couldn’t be rolled back. The instruction was clear, and the agent had it in context.

TL;DR “AI observability” now describes two different jobs. One is watching the LLM apps and AI agents your team ships: prompts, tool calls, token spend, and output quality. The other is putting AI agents to work on your production telemetry so incidents are already being investigated by the time an engineer logs in. Most engineering orgs in 2026 need both, and very few tools cover the two well.

An AI agent doesn’t just answer questions—it takes actions in real systems. As teams across the enterprise build their own agents, governance can’t be an afterthought. Learn how agents work, why they create new risks, and what controls are needed before they act, not after.