Operations | Monitoring | ITSM | DevOps | Cloud

Flaky tests are a serious productivity problem. When tests sometimes pass and sometimes fail without code changes, they undermine trust in your CI pipeline and drain time from engineering teams. Debugging them often turns into a slow process of chasing logs, rerunning builds, and trying to guess what went wrong. This post shows how to quickly detect and fix flaky tests directly in your IDE by chatting with an AI assistant.

Agentic AI is coming to financial services. Elastic provides the data foundation and tools to make it work. In a recent talk at Stanford University, Jamie Dimon, chairman and CEO of JPMorganChase, addressed the firm’s use of AI and ended with mentioning that agentic AI was the next frontier of AI at the firm, inferring it wasn’t ready to be deployed yet. Let’s break down why that may be the case and what the financial services industry can do to become more comfortable with agentic AI.

Magento is a powerful eCommerce platform, trusted by businesses large and small for its flexibility and robust feature set. Yet, anyone who has managed a Magento project knows it can be an adventure—especially during deployment. Misconfigured environments, extension conflicts, and unpredictable performance are just a few issues that can turn a routine release into a hair-pulling session.

When you’re running an enterprise-level Magento store, you can’t afford to leave infrastructure decisions to chance. With large product catalogs, high transaction volumes, and complex integrations, your hosting choices can directly impact your bottom line—and your sanity. The right solution gives you the performance, reliability, and scalability you need while reducing complexities, maintenance overhead, and cost.

There are many vendors, Honeycomb included, where actions on the application can emit a web request that goes to another service for coordination or tracking purposes. Many vendors have pre-built integrations, but some have a fallback that says “Custom Webhook” or similar. If you’re looking to create a full picture of your request flow, you would want these other services to show up in your trace waterfall.

Automating SDLC Governance is one of our key use cases. Kosli gathers all of the evidence your engineering teams need for change management and audit by recording every step in their SDLC, from commit to production, across all of their CI/CD tools. But robust SDLC governance doesn’t just depend on gathering all the necessary data - it also depends on controlling who can add to that data. And that’s exactly what our new access control feature solves.

This is the fourth blog in our Azure Monitoring series, and this time, we’re digging into cost efficiency. Azure makes it easy to scale, but just as easy to overspend. Idle VMs, forgotten disks, and silent data transfer fees add up fast. The result is budget overruns that catch teams off guard and force reactive cuts. This blog breaks down the Azure metrics that actually help you reduce waste, improve visibility, and keep cloud spend aligned with business priorities. Missed our earlier posts?

This is the ninth post in our Azure Monitoring series, and it’s all about taking control of your cloud costs without losing visibility. We’ll unpack why Azure bills tend to spiral, where native tools fall short, and what it really takes to cut spend while keeping performance on point. You’ll walk away with practical ways to spot waste early, act fast, and stay ahead of surprise invoices. Missed the earlier posts? You can catch up anytime.

In today’s era of AI-powered transformation, the excitement around automation is palpable. Technology leaders race to deploy AI agents, build self-healing systems, and create digital environments that seem almost magical in their seamlessness. But amid all the buzz about automation and efficiency, it’s crucial we don’t lose sight of the most powerful force behind any successful transformation: people.