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With SRE AI, Google plans to fully adopt AI and agentic technologies, leveraging AI as a force multiplier while also maintaining control.

Top Tips is a weekly column where we highlight what’s trending in the tech world today and list ways to explore these trends. This week, we’re looking at which tasks you can delegate to AI. We've all struggled to delegate tasks. Whether you're a junior struggling to prioritize your tasks on a daily basis or a manager unsure of assigning responsibilities, you know how messy task delegation can get. Some people just improvise while others have a method to this madness.

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 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.

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.

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.

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.