Operations | Monitoring | ITSM | DevOps | Cloud

Every time you join a video meeting, move data between cloud environments or use an AI-powered application, your traffic travels across a complex network of interconnected infrastructure. At the heart of this global ecosystem sits the internet backbone – and the highest-performing networks within it are known as Tier 1 IP backbones.

Vibe coding made building software feel almost too accessible. You describe what you want, an AI assistant scaffolds it, and a few hours later, something is running. So, it was only a matter of time before developers started asking the obvious question: why should I pay for a monitoring tool when I can just build my own? In all fairness, the DIY instinct is a healthy one. But monitoring is one of the last corners you’d want to cut.

AWS Lambda durable functions let you build long-running, multi-step workflows for use cases such as payment processing, order fulfillment, and AI workflows with human approval. A single durable execution can pause for a wait or callback, retry failed work, and resume in a fresh Lambda invocation without losing its state. The strong resilience provided by durable executions, however, creates an observability challenge because each invocation produces its own telemetry data.

Teams often track operational work across spreadsheets, Slack threads, Jira tickets, and whatever system generated the original alert or signal. This fragmentation makes it difficult to maintain a consistent record of what needs attention, who or what is addressing the issue, and what has already happened. As AI agents take on more responsibility for investigations, triage, and code changes, the number of handoffs grows, making ownership, status, and history even harder to preserve.

Mature experimentation programs eventually have to report the cumulative impact of their shipped changes. The request might come as an ROI story for leadership, a revenue update for finance, or a gut check on the quarter’s progress. The tempting shortcut is to sum the observed lift from each winning experiment and report the total. That naive sum almost always overstates the truth because of a statistical artifact called the winner’s curse.

An agent that only speaks when spoken to is a tool you have to remember to use. Olly has run on a schedule for a while now, working a saved prompt hourly, daily, weekly, or monthly and writing its findings into a chat with its own run history. Those scheduled tasks are now wired into the Coralogix Notification Center, so Olly delivers that output itself, allowing Olly to reach out to Slack or email, out of the box.

How context engineering creates production-ready agentic AI What if the AI strategy you spent the past year building is already being measured by a completely different set of rules? I recently joined Amy Machado, senior research manager at IDC and Jim Malone, senior contributing editor at CIO Marketing Services, for a webinar where we explored how buyer expectations, architectural requirements, and evaluation criteria are shifting as enterprises move from search-driven experiences to agentic AI.

Most build-versus-buy debates assume you're choosing once, at the start, and living with it. Agentic engineering doesn't work that way. The decision shows up at every layer of the stack, and the teams getting it right aren't the ones who picked "build" or "buy" as a philosophy. They're the ones who know which layer is which.