Operations | Monitoring | ITSM | DevOps | Cloud

If you've ever fixed a bug only to watch it come back two weeks later, you already understand why root cause analysis matters. Patching symptoms feels productive - it's not. Getting to the actual cause is what prevents the same issue from eating your team's time over and over again. This guide covers everything you need to know about root cause analysis (RCA) in software testing: what it is, how to do it, which tools help, and where AI is taking it next.

Thousands of organizations rely on open source community builds for infrastructure automation. But if you're tasked with certifying, maintaining, and patching those builds yourself, you know the burden firsthand. The reality is that managing open source internally consumes time, introduces risk, and diverts resources from higher-value initiatives. When critical vulnerabilities emerge, your team scrambles to assess, test, and deploy fixes, all while keeping production environments stable.

Dozens of startups are sprinting to build the next “agentic SIEM” that can autonomously detect, investigate, and respond to threats. They’re well-funded, well-marketed, but structurally hollow. Here’s what it usually looks like: an LLM layer on top of a thin orchestration engine on top of fragmented or customer-hosted data lakes. While it looks impressive in a demo, it quickly falls apart in production. Why? It’s not built on a strong foundation.

Since the rise of Large Language Models (LLMs) like GPT-3 and GPT-4, organizations have been rapidly adopting Agentic AI to automate and enhance their workflows. Agentic AI refers to AI systems that act autonomously, perceiving their environment, making decisions, and taking actions based on that information rather than just reacting to direct human input.

Corporate investment in Generative AI is outpacing value realization. While Large Language Models (LLMs) possess vast general reasoning capabilities, they suffer from a critical blind spot: they are pre-trained on the public internet, yet completely blind to your enterprise reality. This context gap renders even the most advanced models ineffective, forcing them to guess (hallucinate) rather than reason based on your specific business rules.

No one wants to stare at a plant and guess if it needs water. It’s much easier if the plant can say, “I’m thirsty.” A few years ago, we built Plant Buddy using InfluxDB Cloud 2.0. The linked article is still a great guide for cloud-first IoT prototyping as it shows how quickly you can connect devices, store time series data, and build dashboards in the cloud with the previous version of InfluxDB. But this time, the goal was different.

When we started building Checkly's uptime monitoring suite, the goal was to give engineering teams complete visibility across every layer of their stack, from application down to network, in one place. URL, TCP, DNS, and Heartbeat monitors covered a lot of that ground. But one fundamental piece was missing: the ability to simply ping a host and know if it's reachable.

Why do AI agents that look brilliant in a sandbox fail the moment they hit production? For platform leaders, the answer is a lack of environmental parity: the ability to interact with the exact data state and service topology where the actual bugs live. When an agent attempts to modify a schema, optimize a query, or reproduce a bug without access to the real-world data state, it hits the Data Context Gap.