Operations | Monitoring | ITSM | DevOps | Cloud

Shipped: Break down AI spend by your projects and teams, within minutes

Your company talks about AI spend in its own terms, like projects, teams, and products. Now AI Signals can show AI spend in those terms, too. Write a CostFormation rule in CloudZero that groups AI spend by categories you care about, and the new breakdown appears in AI Signals the same day you decide to track it. There’s no waiting for a full processing cycle.

Bring Your Own Identity Provider to Harness MCP

Learn how Harness MCP OAuth proxy mode enables enterprise developers to use their own identity provider without static tokens or shared credentials. OAuth proxy mode lets enterprises run Harness MCP behind their own identity provider, with no static tokens anywhere in the chain. Coding agents write the change. Harness agents take it through production, running the builds, tests, scans, and deployments that follow, with policy enforced on every action.

AI Changed How We Work. It Didn't Change What Your Platform Needs to Do.

If you've spent any time in an engineering Slack lately, you've probably noticed that AI comes up in just about every conversation. Someone's using an assistant to scaffold a new service. Someone else has an agent triaging alerts overnight. And at least one person is asking whether all of this means the team can finally skip the "platform stuff.".

Agentic AI Projects Die in the P&L Review. Here's How Revenue Leaders Can Keep Them Alive

‍‍Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027. Its reasons read like a budget memo: escalating costs, unclear business value, and inadequate risk controls. For revenue leaders, the stakes run in both directions. MGI Research estimates that at least 3% to 5% of revenue leaks between contract and cash, through missed renewals, unbilled usage, and pricing errors. Agents are well suited to close those gaps.

Introducing AI Observability: Trace Every AI Agent Request from Prompt to Pod

TL;DR Logz.io AI Observability provides end-to-end tracing for AI agents and LLM applications. Built on OpenTelemetry, it lets you trace every request from prompt to backend service, visualize multi-step agent execution, isolate root causes across models and infrastructure, and track token usage per step, all alongside your existing logs, metrics, and traces.

Scout Monitoring Is Now in Claude's Connector Directory

A customer messages support: checkout feels slow today. You open Scout, find the endpoint, open a trace, spot the query, copy it into Claude, and ask what’s wrong with it. Each step is quick, but together they add up to a lot of tab switching to answer one question. Scout Monitoring is now available in Claude’s connector directory. Connect it once and Claude can look at your production data directly.

What's new and coming in Redgate Monitor: AI, CIS compliance and cloud migration

This webinar covers what's new and coming in Redgate Monitor: AI with Redgate Assistant and the Redgate Monitor MCP server, CIS benchmark compliance checks and security alerting (Enterprise), AWS cloud migration advice for SQL Server workloads, and the roadmap into 2027. Redgate Monitor has shipped a lot this year, and there's more landing before the end of it. This session walks through what's new, what's coming over the next few months and into 2027, and what it all means for how you run your estate. Roadmap items are current direction, not commitments.

How to Let AI Agents Operate Production Infrastructure Safely

Yes, it can be safe to let an AI agent change production infrastructure, if the agent works through the same controls you would demand of a new engineer: scoped permissions, short-lived credentials, a tamper-proof audit trail, changes that roll out gradually behind health checks, and a fast rollback. The infrastructure also has to be built so that one bad change cannot take everything down.