What Happens When You Replace Legacy Network Tools With AI Advisor?
What happens when you replace legacy network tools with Kentik AI Advisor?
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Engineers Want AI in Observability - With One Catch: 4th Annual Observability Survey by Grafana Labs
Actually useful AI is welcome in observability. AI for the sake of AI is not. In this overview of Grafana Labs’ 4th annual Observability Survey, Marc Chipouras shares what 1,300+ respondents from 76 countries told us about the current state of observability — and what comes next. This year’s survey explores four major themes: The results show strong interest in AI for forecasting, root cause analysis, onboarding, and generating dashboards, alerts, and queries. But when it comes to autonomous action, practitioners are more cautious — and 95% say AI needs to show its work to earn trust.
How A Finance Director Found $30K/Month In AI Savings In 10 Minutes
A real workflow showing how Claude + CloudZero MCP turns plain-English questions into actionable cost intelligence — no dashboards, no tickets, no waiting As Director of Finance and Accounting at a software company, my job can be described simply: Understand what we’re spending, who’s responsible, and whether we can get more efficient. But as anyone who’s had to wrangle AI costs knows, doing so for AI is anything but simple.
Komodor Introduces Extensible, Autonomous Multi-Agent Architecture for AI-Driven Site Reliability Engineering
Out-of-the-box and bring-your-own AI agents that encode operational knowledge boost troubleshooting speed and accuracy across cloud native infrastructure TEL AVIV and SAN FRANCISCO, March 18, 2026 — Komodor, the autonomous AI SRE company for cloud-native infrastructure, today announced a new extensibility framework that transforms its Klaudia AI technology into a universal multi-agent platform for troubleshooting and optimizing performance of complex cloud native infrastructures and applications.
Open standards in 2026: The backbone of modern observability
Open source software and open standards are now an essential part of how organizations maintain their systems. That's not to say they haven't always been important, but the fourth annual Observability Survey, brought to you by Grafana Labs, shows just how deeply the shift to open has taken hold, with 77% of respondents saying open source and open standards are important1 to their observability strategy.
AI in observability in 2026: Huge potential, lingering concerns
The role of AI in observability is evolving rapidly, but the data from our fourth annual Observability Survey makes one thing abundantly clear: the potential is real, and so are the reservations. Practitioners overwhelmingly see value in using AI to help surface anomalies, forecast and spot trends, assist with root cause analysis, and get new users up to speed quicker.
The silent infrastructure tax: why AI agents will break your legacy cloud
For the first time in a decade, humans are the minority on the open web. In 2025, automated traffic officially crossed the Rubicon to account for 51% of all web activity, while generative AI-driven referrals to retail sites surged by a staggering 693% year-over-year. As we move through 2026, these are no longer just "bot" statistics to be handled by a WAF. They represent a fundamental shift in user behavior. The fastest-growing segment of your audience is now agentic.
AppSignal's MCP Server: Connect AI Agents to Your Monitoring Data
Your AI coding assistant already knows your codebase. Now it can know your production environment too. AppSignal's MCP server gives AI agents and AI code editors direct access to your monitoring data — errors, performance metrics, and more — so they can help you debug, investigate and resolve issues without switching context. And with our new public endpoint, getting started is simpler than ever.
What are test hooks in AI-native development?
Summary: A test hook connects a test or lint command to an event in your AI coding agent’s workflow. When the event fires, the agent runs the command automatically. If it fails, the agent’s action is blocked. You can wire your existing test commands into your agent’s lifecycle hooks to get deterministic local validation before code ever reaches CI. AI coding agents write code at a pace where stopping to manually run tests breaks your flow.
The Art of Prompting in AI Test Automation | Harness Blog
E2E Testing Has a New Bottleneck, and It's Not the Code End-to-end (E2E) testing has always been the hardest part of a QA strategy. You're simulating real users, navigating real flows, validating real outcomes across browsers, environments, and data states that never hold still. Traditional test automation tackled this with scripts: rigid, deterministic sequences tied to element selectors and hard-coded values. They worked until the UI changed. Or the data changed.