Operations | Monitoring | ITSM | DevOps | Cloud

Many organizations rely on Oracle Fusion Cloud Applications to run core business workflows across finance, HR, and supply chain operations. Because these SaaS-based applications run on Oracle Cloud Infrastructure (OCI), engineering teams have limited visibility into their performance. Without direct access to the underlying stack, they often lack the signals needed to detect regressions or investigate degraded user experience.

Kubernetes environments generate a constant stream of signals across clusters, nodes, pods, and workloads. For teams that have standardized on OpenTelemetry (OTel), maintaining ownership of that data is critical. But in practice, many observability platforms require translation into vendor-specific data formats, leading to fragmented product experiences, blank dashboards, and uncertainty about data integrity.

LLM applications rarely crash. They degrade quietly. Once these applications are shipped to production, subtle quality failures become harder to catch with traditional signals. Tone shifts, hallucinated details, off-topic responses, and incomplete reasoning can emerge while latency and token usage look stable.

Operators today are navigating unprecedented complexity—rising costs, accelerating customer expectations, and increasingly dynamic networks. In this recent video interview, my colleague Kevin Wade and I explore why AI‑driven automation has shifted from a “nice‑to‑have” technology to a core business requirement for telecom operators and beyond.

Autonomous IT is the operating model where systems detect, decide, and act so your engineers spend less time fighting fires and more time defining what ‘good’ looks like. On a typical day, a mid-size enterprise generates tens of thousands of alerts across on-prem infrastructure, multiple clouds, and AI workloads, including every endpoint. Most of them don’t need a human. A few of them do, and telling the difference, fast enough to matter, is where IT teams are losing ground.

Chris Watts is Head of Enterprise Engineering at OpenRouter, building infrastructure for AI applications. Previously at Amazon and a startup founder. As large language models become core infrastructure for more and more applications, teams are discovering a familiar challenge in a new context: you can't improve what you can't see.

For mobile-first organizations, the distance between a “slow app” and a “resolved ticket” is often filled with guesswork. Mobile performance is notoriously difficult to capture because it lives at the intersection of device hardware, network stability, and local code execution. Today, we are closing that gap with the launch of Coralogix Mobile Performance.

In September 2025, dozens of popular JavaScript packages, like chalk and debug, were compromised on the npm registry. These packages are so ubiquitous they end up in everything: front-end apps, back-end microservices, and CI tooling. Developers didn’t do anything wrong, they just ran the same command they always do: npm install chalk. But then the malware arrived silently. This wasn’t a bug in an operating system. It wasn’t a virus on someone’s laptop.

Coroot's node agent uses eBPF to capture network traffic at the kernel level. It hooks into syscalls like read and write, reads the first bytes of each payload, and detects the protocol: HTTP, MySQL, PostgreSQL, Redis, Kafka, and others. This works for any language and any framework without touching application code. For encrypted traffic, we attach eBPF uprobes to TLS library functions like SSL_write and SSL_read in OpenSSL, crypto/tls in Go, and rustls in Rust.