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Adding Routing Intelligence To Your Observability Stack

Observability has a blind spot, and for most teams it sits at the network layer. You instrument your services, scrape metrics into Prometheus, ship logs somewhere searchable, and build dashboards that tell you when something is wrong inside your infrastructure. But the routing that carries traffic to and from that infrastructure often lives entirely outside the stack, watched through separate tools that do not talk to your alerting. This piece looks at why routing belongs in your observability pipeline and what it takes to get it there.

How Ecommerce Teams Can Scale Local Delivery Without Adding More Dispatch Staff

A store running 20 or 30 local deliveries a day can plan them by hand. Someone pastes the addresses into a maps app, eyeballs a sensible order, screenshots the list to each driver, and the day works out fine. Then the store grows. Ninety orders come in, a third of them carry a two hour window, three are marked urgent, and the two drivers are on different shifts. The same process now eats the whole morning, and the errors it produces cost more than the time it takes.

AI's Role in Enhancing Digital Commerce Operations

Artificial intelligence is quickly becoming a must-have for digital businesses, not just a nice-to-have. For companies looking to sharpen their operations, AI offers powerful ways to predict what's next, smooth out customer interactions, and keep transactions safe. It's not about replacing people, but giving them better tools. This lets teams focus on big-picture strategy while AI crunches data and automates tasks. This shift is changing what's possible in terms of how efficient a business can be, how happy its customers are, and how much it can grow.

Claude outage on July 17, 2026: what happened and how StatusGator caught it early

Claude had a global outage on July 17, 2026, driven by “529 Overloaded” server errors that hit the API, Claude Code, the web app, and the desktop app. It lasted about 1 hour and 32 minutes. StatusGator detected it and sent an Early Warning Signal at 14:30 UTC, 27 minutes before Anthropic acknowledged it at 14:57 UTC.

5 ways agentic AI in ITOps will close the gap between alerts and action

Agentic AI in ITOps has emerged as a practical way to go beyond just detecting incidents. Modern IT teams have invested heavily in observability, yet the gap between detecting an issue and resolving it continues to widen. Three major challenges are driving this shift: This is where agentic AI makes a difference.

Enterprise AI Governance Made Simple with Nexthink's AI Activation Hub

Over the past year, organizations have embraced AI at an extraordinary pace, and Nexthink AI Activation Hub powered by AI Drive has helped customers make sense of that transformation by helping organizations discover the growing wave of AI tools entering the workplace, rapidly triage and govern them, accelerate adoption of approved AI solutions, and measure the impact of AI across the enterprise.

Shipped: Take your AI cost table straight into your own reports

A design partner told us the AI Explorer table needed a way to get data out so it could be saved and shared elsewhere. Now you export the whole view in one click, cost, tokens, cache, and model count all included. The values come through as clean numbers, not text you have to scrub. It’s the same pattern as Explorer, so there’s nothing new to learn.

What Is LLM Observability? A Complete Guide

If you run LLM features in production, your most dangerous failures are the ones your monitoring never flags. Your LLM feature passed every test, and the demo went great. Three weeks after launch, a support ticket lands: the chatbot quoted a refund policy that does not exist. The dashboards are all green, and the same prompt answers correctly when you retry it. This is the blind spot LLM observability exists to close. Your existing tools saw the request come back fast with a clean status code.

The True ROI of Cloud Migration: Modernization, and AI Unlock

For years, the cloud migration business case was framed around one comparison: “Will AWS be cheaper than our data center?” That question still matters, but it is no longer where the value is. The 2017-2024 wave of mass migration is largely complete. Most enterprise workloads are already in the cloud - often in a lift-and-shift state: oversized instances, commercial-OS BYOL, on-prem-shaped network designs, and legacy frameworks that block the next step.