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Building Agent-Friendly CLIs - What we learned at Checkly

Apr 15, 2026 By Checkly In 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.
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How AI-Powered Phishing Is Changing What 'Suspicious Email' Looks Like

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

Apr 15, 2026 By OpsMatters In OpsMatters
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.
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datadog

Offline evaluation for AI agents: Best practices

Apr 14, 2026 By Tom Sobolik In Datadog
If you’re building LLM-powered applications and agents, you’ve probably asked yourself: “How do I know if my changes actually made things better?” You can tweak prompts, adjust temperature settings, or try different models, but it’s not always easy to validate whether version B’s response is better than version A’s. Most teams fly blind in preproduction and rely on user feedback to see how well their application works in the real world.
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qovery

Stopping Kubernetes cloud waste: agentic automation for enterprise fleets

Apr 14, 2026 By Mélanie Dallé In Qovery
Agentic Kubernetes resource reclamation is the practice of using an autonomous control plane to continuously identify, suspend, and delete idle infrastructure across a multi-cloud Kubernetes fleet. It replaces manual cleanup and reactive autoscaling with intent-based policies that act on business state, eliminating the configuration drift and cloud waste typical of unmanaged fleets.
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circleci

Building an agentic content production system with Claude Code

Apr 14, 2026 By Ron Powell In CircleCI
This post by an engineer explains how his team uses the.claude folder in Claude Code. The folder is the hidden directory where you store context files, behavioral rules, and automated workflows so Claude understands how to operate in a specific project. He’d set up coding conventions, tool configs, CI integrations. Very engineering-brained. The tool is called Claude Code, so fair enough. I run a web and content team. We write blog posts, tutorials, and technical guides for a living.
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ScienceLogic

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

Apr 14, 2026 By ScienceLogic In ScienceLogic
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.
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