Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

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.

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.

Every investigation, detection, dashboard, and AI-assisted workflow depends on one thing: data that teams can trust. But as environments grow more distributed, the data behind those experiences gets harder to manage. New applications, cloud services, security tools, infrastructure, and network devices constantly generate machine data, and each new source can introduce new formats, missing fields, inconsistent mappings, and pipeline changes that require expert attention.

IT teams don’t need another place to look for problems. They need a faster way to understand what is happening, decide what to do next, and act before disruption spreads. That has always been the promise of Workspace. It gives IT teams a conversational way to investigate issues, surface insights from Nexthink data, and understand what needs attention across the digital workplace. Now, Workspace is entering its next phase.

To deploy an application we haven't seen before, we need two things out of its repository. One is a Dockerfile that builds it. The other is a service definition saying what runs, which ports it listens on, which databases it needs, and what environment variables it expects. Customers arrive with a git URL, and we figure out the rest. We even do the tricky task of generating a Dockerfile if it doesn't already exist in the codebase. We've built a solution for this twice already.