Operations | Monitoring | ITSM | DevOps | Cloud

Resolve Webinar: Introducing AgentLab: The Foundation of the Autonomous Service Desk

Most service desks still operate across fragmented systems. A single ticket can touch 4–7 tools, often more, slowing resolution and increasing cost. Copilots suggest. Traditional automation executes fixed paths. Neither closes the loop. AgentLab changes that. In this webinar, we introduce a new model built on agentic AI and orchestration. One where AI agents don’t just assist. They act, adapt, and resolve.

Google Cloud Next '26 Recap: AI, Efficiency, and the Rise of Frictionless Delivery | Harness Blog

‍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. ‍

Shadow IT Is Back - And Vibe Coding Made It 10x Worse

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: When "sounds right" isn't right

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.

DORA Metrics in the AI Era: Why Deployment Isn't Faster

DORA metrics in the AI era reveal a paradox: PR volume is climbing, but deployment frequency is staying flat. In this talk, GitKraken's Director of Product Jeff Schinella breaks down why AI-accelerated code generation is creating a review bottleneck that your DORA metrics can't fully explain on their own. Jeff walks through how PR metrics (cycle time, first response time, code churn, and PR size) serve as the leading indicators behind your DORA data. If your deployment frequency is flat while PR counts go up, the bottleneck isn't your devs. It's your review capacity.

Bindplane Now Ships With a Native AI Skill - Bring Your Own Agent

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.

Your Team is Using Claude Code. Do You Know What It's Costing You?

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.

Moving On From MCP: How We Built the Bindplane AI Skill

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.

AI writes the code. Who delivers it safely? | Harness Blog

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.