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Checklist: how to reduce environment drift without slowing devs or AI agents
Environment drift persists when teams standardize code but leave infrastructure, data, and access decisions to individual teams and manual setup. Most teams know their environments are not identical. What they underestimate is how quietly the gap widens. A database version is out of sync between production and staging; an environment variable is added manually to one server but never tracked; a cron job runs in production but was never captured in the dev config.
Bridging AI and Infrastructure: Introducing the Megaport MCP Server for Agentic Networking
Discover the Megaport MCP Server and how it enables AI-powered, agentic networking through natural language access to network infrastructure. By Miwa Fujii, Community Manager - Terraform and Ryan Tucker, Solutions Architect In the cloud networking era, we’ve moved from manual configurations in the Portal to Infrastructure as Code (IaC), Terraform. But the next frontier isn’t just code, it’s intelligence. We are pleased to announce the release of the Megaport MCP Server (Open Beta).
Beyond tokens per watt - using Ubuntu 26.04 LTS for AI
Tokens per watt (TpW) – the measure of useful AI work produced per watt of energy consumed – is the metric at top of mind for CEOs, heads of AI, and infrastructure teams alike. With the tremendous cost of GPU clusters, extracting as much value as possible from the expense is critical. But in the pursuit of tokens, it’s important to remember that hardware efficiency isn’t the only factor influencing data center operating costs, or the output of useful, revenue-generating AI work.
The AI Code Explosion: Why Your Mocking Strategy is Breaking Down
The rise of AI-assisted coding has transformed how software is built. With tools generating entire features in seconds, the bottleneck is no longer writing code—it’s verifying it. Because AI can generate boilerplate and handle API integrations instantly, more service changes are being pushed into authentication logic, API calls, and configurations. Teams desperately need a way to verify these changes before merging, especially when the code touches external dependencies.
Rocky reads your stack, not just your check
You get an infrastructure alert. Something's off, so you task your agent to investigate, and it ends up grepping through the logs. You both wonder if there's customer impact. There’s a simple rule: AI agents in the incident loop are only as good as the signal you feed them. And logs alone aren't a great signal.
Introducing Bits Agent Builder: Build agentic workflows for alert response and remediation
Building automated workflows that adapt to real-world complexity can be a challenge. As systems scale and scenarios multiply, teams often end up hardcoding endless logic branches just to handle every potential outcome. That’s why we’re introducing Bits Agent Builder, a powerful new tool that lets you create custom AI agents that are fully hosted by Datadog.
AI Observability Deep Dive Demo | Grafana Cloud
Grafana AI Observability is our new database and platform for observing AI Agents. Over the past year at Grafana Labs, we built Agents and we needed a way to understand how they are performing, what are the costs associated with them, what's the error rate or time to the first token as well as how they are behaving. Grafana Staff Engineer, Ivana Hučková provides a deep dive demo on how Grafana AI Observability connects our experience building Agents with our experience building observability systems.
Grafana Assistant Context Offloading
Context Offloading is a pipeline solution for managing Observability with AI Agents. If you are building AI Agents that work with real data, the context window can very easily get filled with bloated context that the Agent does not really need. Sven demonstrates "Context Offloading", a solution that stores the JSON result and sends only the summary of the JSON blob, making the LLM loop performance much quicker and keeping your context window small.
CLI and MCP Basics for AI Agents (The Context Window #4)
MCP vs CLI: which one should your AI agent actually use? We get into it with Grafana's cloud and OSS MCP servers and gcx.