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The model your team relies on today is unlikely to be the one you're relying on a year from now. If your team's process for shipping AI-assisted code is built around a specific model, coding assistant, or a vendor's take on an autonomous agent, you are not building infrastructure. You are building something you will tear out and rebuild the next time the leaderboard shifts.

The phrase “AIOps automation” now appears across observability platforms, ITSM suites, orchestration products, runbook tools, and general automation software. The label alone says little about what happens after detection. Some products create a ticket. Some launch a script. Others gather live context, choose an approved response path, execute across several systems, verify recovery, and update the operational record.

If you were anywhere near LinkedIn last week, you probably saw us launch AI Signals. We weren’t exactly quiet about it. (Press release, a couple of blog posts, and more social posts than we’d like to admit. Sorry about your feed.) We covered the why behind AI Signals already, but I wanted to actually walk you through what you’re seeing on the screen. Sooner or later someone asks what the company spent on AI last month.

Purview retention policies can now archive inactive SharePoint files into Microsoft 365 Archive. What MC1472601 and MC1325441 change, the Copilot indexing trade-off, and what to configure before the rollout reaches your tenant.

An error budget is the unreliability an SLO allows. Learn to calculate it, track burn rate, set multiwindow alerts in PromQL and write an error budget policy. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

I built GitBench because I was curious about how well models did git things. It turns out that it was a good idea because some models will surprise you. Some of the frontier models were remarkably bad and some cheap open models punched above their weight class. But, as they say, “the times they are a changin’”. Before I go too deep into the latest results, you may want to read up on GitBench and what it is and why I built it in the previous blog post.

A practical comparison of the best observability tools in 2026: Last9, Datadog, Grafana Cloud, New Relic, Dynatrace, Honeycomb, Coralogix and SigNoz. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.