Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

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.

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.

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.

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.

For many EUC and Digital Workplace leaders, the challenge with digital employee experience (DEX) isn’t the technology, it’s building alignment. You can see the data. You know where friction exists. You can quantify disruption, productivity loss, and inefficiencies. But you struggle to achieve your targets, because you need buy in from other teams, and right now, they don’t want to hear anything about DEX. Security has different priorities. Application owners are focused on releases.