Operations | Monitoring | ITSM | DevOps | Cloud

Diff-erent Perspectives: How Specialized LLM Personas Catch More Bugs

We’ve built a multi-LLM PR reviewer that runs on every pull request in a couple of our own repos. Two independent models look at each change in parallel, each wearing a set of “persona hats” tuned to a specific area of the codebase. They compare notes, duplicates get stripped out, and the PR author ends up with a single review comment rather than a wall of noise.

AI Meeting Bots Were Just the Beginning. Meet the AI Collaborator

Why the next era of enterprise AI isn’t about note-taking — it’s about digital workers who actually show up and do the work. There’s a moment every IT operations leader knows well. A critical incident hits at 2 PM on a Tuesday. Within minutes, a war room meeting spins up — a Google Meet or Teams call crowded with network engineers, SRE leads, cloud architects, and storage admins, all staring at dashboards and talking over each other. Someone is manually pulling syslog data.

Debug frontend issues with AI: Real user monitoring meets the Coralogix MCP server

It is 2 AM. Someone on-call gets paged. Conversion rates on the checkout page dropped 30 percent in the last hour. The immediate questions are familiar. Is this a JavaScript error? A slow API call? A broken third-party script? A performance regression that never throws an exception but quietly drives users away? In most teams, answering those questions is not hard because the data is missing. It is hard because the investigation is split across too many places.

Dark Code: The AI-Generated Software Nobody Understands

The biggest risk to your product isn’t AI-generated code that doesn’t work. It’s generated code that seems fine. AI doesn’t optimize for correctness. It creates something passable. Something that passes the smell test. And when everybody in the industry is pushed to move faster and do more with less, you end up shipping software that looks correct. It passed your quick visual check. It passed all the tests. But no one ever fully understood it.

Beyond AI Vibes: Deterministic Foundations for Agentic Coding

Every week there is another model drop, another agent framework, and another workflow tweak you are supposed to evaluate. Meanwhile, the largest companies, the ones operating at the highest scale and leaning hardest on AI, are also the ones making headlines for reliability strain: capacity limits, outages, and services that buckle under load.

Autonomous AI for Cloud-Native Cost Optimization: Balancing FinOps and Performance SLAs

Platform Engineering leaders are caught between two competing imperatives. You’re under pressure to flatten cloud spend but your team is still provisioning defensively because nobody wants to be the person who causes a production incident. You try to optimize, but six months later, when someone pulls a report, nothing has changed.

Your AI Agents Are Autonomous. But Are They Accountable?

Why accountability, not capability, is the real bottleneck for enterprise agentic AI, and what security leaders need to do about it before regulators force the issue. Every enterprise is building AI agents. Marketing has one summarizing campaign performance. Engineering has one triaging incidents. Customer support has one resolving tickets. Finance has one processing invoices.

MCP Apps: On Call Compensation Report and Service Dependency Graph

This April, PagerDuty's MCP server expands with powerful new capabilities across Analytics & Reporting and Business Services. Teams can now surface aggregate incident data, service metrics, and team metrics — giving operators instant access to the operational insights that matter most. On the Business Services side, the release adds business service dependencies, subscriber management, impacted services analysis, and priority mapping. Rounding out the release are two new MCP Apps (on our experimental branch): Service Dependency graph. and an On-call Compensation report.