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A more reliable agent that learns your schema, queries your data, and investigates alerts - that’s what’s possible with this release of our MCP server v1.3.0.

AI agents tend to function as black boxes, and it can be difficult to trace and understand agent workflows end-to-end in order to characterize performance. Particularly, you need visibility into the following: By tracing full agent runs with LLM Observability, Datadog AI Agent Monitoring enables you to visualize workflows with flame graphs and quickly spot sources of failures and latency.

AI coding tools made code generation faster. Measuring what actually ships is the hard part. Over the last eighteen months, tools like Cursor, Claude Code, Copilot, and Windsurf have fundamentally changed how software gets built. AI-generated pull requests are increasing, developers are producing more code than ever before, and workflows that once took hours now happen in minutes. But most organizations struggle to clearly explain what that investment is actually producing.

Gartner expects worldwide AI software spending to hit $2.59 trillion in 2026, 47% more than organizations spent last year. The dollars are real and growing fast. But most organizations still can't measure the ROI of that spend. The problem has two sides: developers and infrastructure. On the developer side, engineers are using AI to write nearly every line of new code, and leaders have no way to tell whether that spend is producing software that ships.

Companies are shipping AI features at a pace cloud teams have rarely seen. New agents, new copilots, new flows powered by language models, all moving from prototype to production in weeks. The spend that comes with it is real and accelerating, and most teams are seeing it on the invoice before they see it anywhere else. The question is no longer how much you're spending on AI. It's whether each dollar is producing a real outcome, and whether you can govern that spend before the next invoice arrives.

Gartner forecasts $2.6 trillion in global AI spend this year. Most of it lands in invoices that don’t connect dollars to the developers who spent them, the customers they served, or the features they shipped. AI billing is a mess. CloudZero is the financial control plane for AI spend. Three capabilities, available today, reveal the by-customer, feature, and developer ROI of AI: 1. Real-time Spend: Capture every dollar spent on AI, at the source. 2.

The ability to schedule regular tasks, such as cron jobs, has been around for decades. So why are we still running the same AI prompts by hand every day? As you use Grafana Assistant, our AI-powered observability agent, to stay on top of the state of your system, you likely find yourself asking the same questions. Maybe you want to know what changed overnight, or whether yesterday's deployment hurt latency, or which dashboards or skills are drifting out of date.