Operations | Monitoring | ITSM | DevOps | Cloud

RSAC 2026 Conference is right around the corner, and we're unveiling new security capabilities at Booth N-5285. Book a 15-minute slot if you want an in-person walkthrough!

In Part 1, we looked at how AI has reduced the cost of building monitoring tools. Then in Part 2, we explored the operational and economic burden of owning them. Now we need to talk about something deeper. Because the real shift isn’t just economic; it’s structural. AI isn’t just helping engineers write code faster. It’s accelerating the entire software ecosystem; including how monitoring tools are built, maintained, and trusted.

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.

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.

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.

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.

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.

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.