Operations | Monitoring | ITSM | DevOps | Cloud

Is Generative AI Eroding Our Ability to Think?

In aviation, there's a well-documented issue known as "automation addiction." As cockpit systems became more advanced, pilots gradually shifted from actively flying aircraft to supervising automated controls. Everything worked smoothly-until a system malfunctioned. Investigations revealed a troubling pattern: even experienced pilots sometimes struggled with basic manual maneuvers. Their hands remembered less because their brains had practiced less.

AI Engineering at incident.io

Working on AI in incident management means there's no playbook. No million blogs. Just building at the forefront of what's possible with AI models.In this video, Martha, Product Engineer on our AI team, talks about what it's really like working with AI that helps engineers respond to incidents faster. This covers the shift from traditional engineering, learning the personalities of different AI models, and why you need to embrace constant change when new models drop all the time.

The Need for Clean in the AI Era

In the AI era, software and new models are being born at a breakneck pace—but they’re also bringing a lot of “baggage” into the world. While AI coding agents are busy accelerating innovation, they’re also excellent at generating a massive byproduct: “digital dust.” Between obsolete releases, orphaned dependencies, and massive model versions, your repository may soon start to look more like a digital junk drawer than a streamlined machine.

How to Make AI-Generated Code Reliable with Runtime Context

AI coding assistants like Cursor and Claude Code are driving massive productivity gains, yet they have introduced a critical validation gap in the software delivery lifecycle. While these tools excel at generating syntax, they lack visibility into live production environments. This article explains how Runtime Context, the missing nervous system of AI development, secures production by moving from probabilistic guessing to deterministic, live code validation.

The AI infrastructure gap: why agents fail on fragmented stacks

The initial hype of AI agents is hitting a hard reality: a clever prompt is not a production strategy. As organizations move from experimentation to operationalizing AI in 2026, a systemic bottleneck has emerged: It is not the model's intelligence; it is the model’s context and its access to the right tools. When an AI agent lacks access to live, grounded platform data, it guesses.

Use AI to turn any JSON API into a dashboard in minutes with the Infinity data source plugin and Grafana Assistant

The internet is full of fascinating data just waiting to be visualized and queried. And with the latest update to Grafana Cloud, you can start doing it in minutes. Through public APIs, you can access information about global earthquake activity, weather forecasts, music catalogs, and millions of other datasets. And then there's all the data that sits inside company APIs, partner services, and internal platforms that power everyday products and operations.

AI Merge Conflict Resolution + Commit Messages in GitKraken Desktop

AI-assisted merge conflict resolution is changing how developers handle Git workflows. Watch GitKraken Ambassador Kevin Bost demonstrate AI-powered features that eliminate merge conflict dread, clean up messy commit history, and generate contextual commit messages in seconds.

The Current State of Content Negotiation for AI Agents (Feb 2026)

The web was built for humans, but now the agents are taking over. Humans look at a web page and see content rendered by their browser. AI agents see 180,000 tokens of nav bars, footers, and div soup — burning through their context window on junk that makes them slower and stupider. The web needs to evolve, and we as developers are driving the shift. AI agents like Claude Code, Cursor, Codex, and Gemini are how we interact with documentation, CLIs, and products today.

The 2025 Wake-Up Call for Engineering Teams

For years, organizations tried to solve operational pain by collecting more data, adding more dashboards, and consolidating more tools. But 2025 exposed a deeper mismatch. Systems had become more distributed, AI-assisted, and interdependent than ever before, while teams had shrunk and on-call pressure had intensified. This wasn’t a tooling failure. It was an architectural and cognitive one.