What is agentic telemetry?
You can't ask questions of data you don't have. Neither can your agents.
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How to Connect Cursor to CircleCI: AI-Powered CI/CD Debugging with MCP
Stop context-switching between your IDE and CI dashboard. This video shows you how to connect Cursor to CircleCI using the CircleCI MCP server so your AI agent can read pipeline failures, validate configs, and trigger builds without leaving your editor. In this demo, we introduce a bug, let CI catch it, and watch the agent diagnose and fix it autonomously through a full green pipeline. No manual log hunting required.
From building agent infrastructure to building agents
TL;DR Agent projects turn into infrastructure projects fast. Managed agent platforms handle memory, integrations, scheduling, and scaling so builders can focus on the agent itself.
What is an AI sandbox? A developer's guide
An AI sandbox is an isolated environment where code from an AI coding agent runs without direct access to your machine or your production systems. If the agent runs a destructive command or a mistaken script, the damage stays contained inside the sandbox. Developers use AI sandboxing to let agents execute code freely while keeping the host and its credentials out of reach.
Meet GCX: Give Your AI Coding Agent Production Context
Your AI agents are only as good as the context it has. Without access to what's happening in production, it can only make educated guesses. Chapters: In this video, you'll meet GCX. The bridge between AI coding agents like Claude Code, Codex, Cursor, and your Grafana observability stack. You will learn how GCX securely gives AI agents access to metrics, logs, traces, dashboards, and other production telemetry so they can investigate issues, answer questions, and help you debug with real operational context.
AI Week: Tuesday announcements
Tuesday is about Building and implementation. Here's what we're announcing today! Check out grafana.ai for more details on everything we announce this week.
The New AI Mandate: Smarter Usage, Lower AI Compliance Risk, Better Outcomes
For the last two years, enterprise AI strategy has largely revolved around one message: use AI as much as possible. CIOs encouraged experimentation, CFOs approved budgets, and organizations pushed employees to adopt tools like ChatGPT, Claude, Copilot, and Gemini in the hope that productivity gains would naturally follow. The prevailing assumption was that simply increasing usage would accelerate innovation and unlock efficiency across the organization. But the enterprise conversation is changing quickly.
Gemini Models are Misunderstood
Recently, Google released a couple new Gemini models. They were Gemini 3.6 Flash, 3.5 Flash Lite, and 3.5 Flash Cyber. Many people on the internet roasted Gemini Flash and Flash Lite for their less-than-frontier performance. This makes sense when you consider that OpenAI and Anthropic have been sparring back and forth for first place for quite a while but Gemini almost never gets that first place position. After all, this is Google we are talking about.
How to prove the business value of AI
Proving AI business value means sorting every AI investment into one of four buckets - revenue growth, cost avoidance, productivity gain, or risk reduction - then tracking spend at the unit level (per feature, customer, or team) so each dollar has a traceable return. Most companies measure one bucket well and leave the rest unattributed. That gap is why the same AI deployment can look like a $40M win and a public reversal at the same time.