Operations | Monitoring | ITSM | DevOps | Cloud

Building Agent-Friendly CLIs - What we learned at Checkly

Building Agent-Friendly CLIs: Why Your AI Agent Already Loves the Checkly CLI Stefan explains why products, docs, and CLIs must be AI-ready as coding agents rapidly become primary users of the Checkly CLI. He outlines key CLI features for agent workflows: Stefan demos how an agent initializes project-tailored Checkly setup from scratch without any human intervention and also shows how agents can entirely automate the incident life cylce from resolution to status page communication.

GitKraken Desktop 12.0 Release: Agent Sessions, Terminal Performance Boosts, and More!

If you're running Claude Code, Codex, or Gemini, managing multiple sessions means one terminal per agent, status checks by window-switching, and worktree setup from scratch every time. GitKraken Desktop 12.0 adds structure to that workflow. What's new: Works with Claude Code, Codex CLI, Copilot CLI, Gemini CLI, and OpenCode.

AppSignal MCP Now Supports OAuth - and GitHub Copilot

When we launched AppSignal MCP in beta, OAuth was on the roadmap but not yet shipped. We were issuing static bearer tokens — enough to connect Claude Desktop, Cursor, and Windsurf, but not the one-click install path in the MCP Registry, and not GitHub Copilot's recommended setup. That's fixed.

How AI-Powered Phishing Is Changing What 'Suspicious Email' Looks Like

For years, spotting a phishing email was almost a checklist exercise. Look for typos, watch for broken grammar, be suspicious of generic greetings like "Dear user," and check if the sender's address looks strange. That mental model worked because phishing emails actually looked bad. Which is no longer true. With the rise of AI, attackers can generate emails that are grammatically perfect, context-aware, and indistinguishable from legitimate business communication. The obvious red flags are gone. What used to look suspicious now looks completely normal.

The Trust Layer: Why Enterprise AI Needs a Gateway Before It Needs More Models

Enterprise AI does not have a model problem. It has a trust problem. Before organizations invest in larger models or additional agents, they need a control layer that governs how those agents operate inside production systems. Without that layer, autonomy does not scale. If you talk to any enterprise leader right now, you’ll hear the same question.