Operations | Monitoring | ITSM | DevOps | Cloud

‍Summary: Google Cloud Next ’26 focused on the future of software delivery, emphasizing that AI, platform consolidation, and an urgent push toward efficiency are reshaping the Software Development Life Cycle (SDLC). The key takeaway from the event was that organizations are moving from AI experimentation to operationalization, actively consolidating fragmented tools onto end-to-end platforms that embed AI for control, intelligence, and speed. ‍

AI coding tools are the new Shadow IT - but instead of rogue Trello boards, they have OAuth access to your code repos, cloud accounts, and production databases. Here's what's already gone wrong, and how platform engineering fixes it. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

Top Tips is a weekly column where we highlight what’s trending in the tech world today and list ways to explore these trends. This week, we’re looking at why convincing AI answers can still be wrong and how to catch them before they slip through. AI doesn’t fail the way it used to. It doesn’t give obviously wrong answers. It gives answers that are just right enough to trust. And that’s exactly why we stop questioning it. It fits into our workflow so easily.

Today we're rolling out the Bindplane AI Skill, a built-in capability of the Bindplane CLI (v1.98+) that teaches your favorite AI coding tool how to work with Bindplane — natively, accurately, and without the setup headaches of traditional integrations. Read Part 2 of the Bindplane AI Skill series to learn more about how we built it and how it works with real-life examples.

The first two weeks of Claude Code are exciting. The third week is when you realize you don’t have visibility into what it’s doing or what it’s costing you. You would not run a production service without metrics, logs, and dashboards or deploy an API without knowing its latency, error rate, or cost per request.

If you've spent any time wiring AI coding agents into developer platforms over the last year, you've probably reached for MCP. We did too. And after enough sessions watching context windows balloon and tool calls misfire, we started looking for something different. This is the story of what we built instead — a native AI skill for the Bindplane CLI — and the engineering decisions behind it.

The question for enterprise AI in 2026 is no longer just which model. It’s which harness. An agent harness is the system around the model. It decides what the agent remembers, what context it sees, what tools it can call, what it is allowed to do, and what happens when it is wrong. The model provides intelligence. The harness provides control. This is where the real engineering is happening.

TLDR: Today, Harness is introducing the Harness Cursor Plugin, bringing the power of the Harness AI-native software delivery platform directly into Cursor. This integration, along with the Harness Secure AI Coding hook for Cursor, allows developers and AI agents to move from code changes to vulnerability detection, CI/CD execution, security validation, approvals, deployments, and operational insight without leaving the editor. AI has completely changed how we write code.

Training a model in a notebook is easy. What breaks teams is the step after, serving it reliably without haemorrhaging cloud budget or burying your SREs in YAML. The common trap: picking a platform that handles the model but not the surrounding stack. An AI deployment platform should orchestrate the full application graph (inference endpoints, vector databases, caching layers, and frontends) inside a single VPC, with GPU autoscaling that doesn't require a dedicated platform engineer to babysit.