Operations | Monitoring | ITSM | DevOps | Cloud

The AI-powered documentation search on the Alloy Software docs site has been updated, and it’s noticeably better.

Deployment failures shouldn't require Kubernetes expertise to fix. The new AI Copilot analyzes your logs, events, and config to pinpoint the root cause and propose a fix.. in seconds.

KubeCon + CloudNativeCon Europe 2026 recently brought the cloud native community to Amsterdam. We were there all week bouncing between the booth, a Braintrust event with engineering leaders from across the community, and more hallway conversations than we can count. One talking point dominated the week: AI is shipping code faster than most engineering orgs can govern it. It also became clear that we weren't the only ones talking about this challenge.

The pace of AI-assisted development has outgrown how most teams actually ship. Harness is closing that gap. Engineering teams are generating more shippable code than ever before — and today, Harness is shipping five new capabilities designed to help teams release confidently. AI coding assistants lowered the barrier to writing software, and the volume of changes moving through delivery pipelines has grown accordingly. But the release process itself hasn't kept pace.

While AI have usage has been growing steadily for the last several years, the LLM models noticeably improved around the end of 2025. Specifically, they become more viable for software development. We are seeing the results. The feature and product delivery has picked up. One way to visualize this is by looking at the number of pull requests for your organization / software development teams. This chart shows the number of Github pull requests created by a team. Can you spot when AI usage increased?

Since releasing our hosted MCP server last year, we've been thrilled to see customers not just adopt it but build Honeycomb deeply into their agentic development and observability workflows. Users have embraced it, leveraging Honeycomb to stay in conversation with their code and understand how it runs in production.

Your AI coding agent just made every test pass. Ship it, right? Not so fast. A growing class of AI-generated bugs doesn’t come from writing bad code. It comes from the AI changing working code to accommodate its own mistakes. This isn’t a theoretical risk. It’s happening now, in production codebases, and it’s harder to catch than any bug the AI might introduce from scratch.

At SaaS Metrics Palooza 2025, CloudZero CEO Phil Pergola delivered a keynote on the software industry’s most pressing question: can SaaS survive the AI revolution, or will AI rewrite the SaaS playbook outright? Phil’s answer wasn’t doom and gloom, but he didn’t sugarcoat the challenges. “Churn rates are up,” he told moderator Ray Rike of Benchmarkit on Oct. 9, 2025. “The payback from a customer acquisition cost perspective is taking longer.