Operations | Monitoring | ITSM | DevOps | Cloud

A virtual agent answers only what it has been given to work with. When an employee asks about a VPN error, a software license request, or a password reset process that changed last quarter, the agent's response is only as good as the knowledge base behind it. Gaps in that knowledge base show up as deflected tickets that bounce back to a human, generic responses that miss the specific issue, or silence on topics employees ask about every week.

"Every website was once designed for people. That assumption is beginning to change." For nearly three decades, the internet has worked in a predictable way. Whenever we wanted to know something, we searched for it, clicked through a few websites, compared information, and made a decision. Whether it was buying a new phone, planning a vacation, or researching software for work, businesses knew exactly how people behaved online.

Your monitoring stack never sleeps. Datadog fires a spike, ServiceNow spins up a ticket, your RMM flags a failed backup, and every one of those signals competes for attention across email, dashboards, and chat channels. For IT Ops teams running on-call rotations, the volume itself becomes the problem. Alert fatigue sets in, critical notifications blend into the noise, and the one incident that matters at 3 a.m. gets buried under a hundred that don’t. The cost is real.

Every developer knows the moment: CI goes red, and you face a choice. Open the browser and click through the web UI to the run, the workflow, the job, the step, the log line. Or stay in the terminal, where the fix is going to happen anyway. The new CircleCI CLI exists so you can stay. It’s 1.0, it’s in beta, and it’s a ground-up rewrite in Go, not an iteration on the CLI we’ve shipped for years.

AI agents are changing how software gets built, but the infrastructure around them hasn’t caught up. Agents burn through tokens on noise. They take actions they shouldn’t. Context evaporates between releases. And most delivery pipelines were never designed for the pace and volume of agentic development.

As businesses in India embrace the cloud, a critical question looms: Where does your data really live, and who controls it? India's cloud market is estimated at USD 26.43 billion in 2026, growing at 21% annually with projections reaching USD 68.82 billion by 2031. This rapid expansion underscores the strategic importance of cloud infrastructure in the country, but with growth comes growing urgency.

Every IT leader is under pressure to show AI results. Budgets are approved, pilots are launched, and vendors promise transformation within a quarter. Some teams are already running AI agents in production, resolving tickets and answering employees without human intervention. Others are still stuck in proof-of-concept purgatory, six months into a rollout with nothing to show a board. The thing is, AI doesn't fix what's broken in an IT operation, it multiplies what's already there.

GitBench is our attempt to see how well LLMs handle git-specific tasks. We put together a suite of 17 benchmark categories with a total of 204 specific test cases. We ran these benchmarks against 43 (and counting) different models with all of their supported effort/reasoning levels.

There’s a difference between seeing a problem and preventing one is not a question of tooling. It is a question of operational posture. Across eleven operator interviews at Nexus Live, a consistent pattern emerged. Teams are not struggling because they lack visibility. They are struggling because visibility alone does not produce confidence. Alert floods, late root cause discovery, and 3am escalations have become normalized in hybrid environments. The result is not just fatigue.