Operations | Monitoring | ITSM | DevOps | Cloud

Why AI-Generated code still needs tests

AI has made life easier for us developers. Dozens of AI tools are getting more advanced every day, shortening and speeding up the development cycle. Converting an idea into a concrete MVP is faster than ever before. Intelligent models turn your ideas into a working project with just a few prompts. End users or product owners can see the application, use the beta version, and give feedback on the spot.

Measuring What Matters in AI Applications

AI has become incredibly sophisticated and changed the way we build and power applications. But it has also introduced volatile complexity that challenges how we ensure reliability and protect user experiences. Join our panel of experts as they discuss the realities of building AI-powered applications. They’ll talk about how testing has evolved, share insights into how they do evals and experiments, and offer advice on how to get better visibility into how your AI is performing.

39% of Coralogix sessions are now headless

We set out to understand how customers were using Coralogix from outside the web app. Olly answers questions about a system in plain language. Coding agents query logs, examine traces, read metrics and configure the platform through the Coralogix CLI and MCP server. We wanted to see how much of this was becoming part of everyday work. In the week of 20–26 October, 39% of Coralogix sessions included Olly, the CLI or MCP.

AI Signals Demo: See Exactly What Your AI Spend Is Buying | CloudZero

See who’s spending on AI, on which models, for what work, and what you got for it. Most companies can tell you what they spent on AI last month. Very few can tell you what it bought. In this demo, we show how AI Signals captures AI usage as it happens and ties every dollar to the person, team, and work behind it, across every model and provider.

Five things you can do with Sentry's Agent Tracing

AI-powered apps are hard to debug without observability. From one run to another, AI produces different results, goes down different paths, and you’re none the wiser as to why it’s happening. Agent Tracing bridges the gap between prompt, tool calls and final output, all within the context of your app. If things break (it always does at some point, we know from experience), you get everything in one complete trace.

AI cost per customer: measure it and protect your margins

AI cost per customer is each customer's attributed share of your AI spend: API tokens, inference infrastructure, and per-seat AI tools, measured against that customer's revenue. It matters because AI usage is wildly uneven across customers, so healthy blended margins can hide individual customers you serve at a loss. It's cost to serve, not acquisition cost. Cost per customer is the metric CloudZero was built around.

Announcing SOC 1, FedRAMP 20x Class A, PCI, and HIPAA Milestones At Harness

Explore Harness' latest compliance milestones: SOC 1, FedRAMP 20x Class A, PCI DSS, and HIPAA assessments across its platform and runtime security. Harness announced four compliance milestones: its first SOC 1 audit, FedRAMP 20x Class A certification, and first PCI DSS and HIPAA Security Rule assessments. SOC 1 and FedRAMP 20x Class A apply to the Harness Autonomous SDLC Platform. PCI DSS and HIPAA apply to the Runtime Protection Agent.

Managing AI Video Assets from Experiment to Approved Release

The difficult moment in an AI video project often arrives after generation. Someone drops a promising clip into a team chat, another person requests a change, and a third person downloads a different version for publication. By the next morning, nobody is certain which file was approved. For teams already managing software releases, this is a familiar coordination problem with an unfamiliar kind of artifact.

Working from home in the AI era: Advantages, disadvantages, and the way forward

Remote work has changed more than where we work. It has reshaped how teams collaborate, how organizations manage performance, and how employees balance professional and personal responsibilities. Now, AI is adding another dimension to this transformation. From AI assistants that summarize meetings to automation tools that handle repetitive tasks, technology is making remote work more efficient in several ways.

Scaling Agentic ITOps: From AI pilots to autonomous IT operations

For enterprise IT leaders, the question is no longer whether AI agents can help run IT operations, but how to scale them beyond pilots and into the everyday work of keeping the business online. It’s no coincidence that this year’s Gartner IT Infrastructure, Operations & Cloud Strategies Conference (IOCS) opens with a keynote dedicated to scaling agentic AI to enable autonomous IT operations. At BigPanda, we’ve seen a shift from the ITOps leaders we work with every day.