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The latest News and Information on Continuous Integration and Development, and related technologies.

Automate all the things: How to use Grafana Cloud's AI to relieve the operational burden

Continuous integration and continuous delivery (CI/CD) have dramatically changed how we ship software. But once code reaches production, the operational work is still surprisingly manual. Engineers continually monitor systems, investigate unexpected behavior, and decide which issues require action. And that is where the next opportunity for AI-driven automation lies. For example, in today's CI/CD workflows, someone refreshes the pipeline page to see whether the queue has moved.

Golden paths: how to ship securely without slowing developers

The secure path and the easy path should be the same path. Ralph McTeggart (Principal Engineer), Alex Franzmann (Customer Onboarding Team Lead), and Claire McDyre (Product Manager) at Cloudsmith lay out how platform teams can deliver supply chain security as a capability rather than a checklist. The argument runs from first principles: make a private registry the default, automate policy enforcement at the global level, and extend that same logic to compliance – so SBOM generation happens in the pipeline, not as a developer's manual task.

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.

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.

Supply chain hit: the first hours are about "am I affected?"

The first two to three hours after a supply chain compromise aren't about remediation – they're about figuring out whether you're affected at all. That requires two things: a feed of compromised packages you can cross-reference against what you're using, and an audit trail showing which developers pulled which packages and when. Teams that can answer "are we affected?" in 30 minutes have that data pre-built into their platform. Teams that don't are still piecing it together at hour three. Protect your supply chain with Cloudsmith.

What is a remote MCP server?

As development shifts toward agentic workflows, an AI agent is only as capable as the systems it can reach. Local MCP servers allow desktop CLI and IDE agents to execute multi-step tasks on your machine. But remote MCP servers (also called hosted MCP servers) extend that reach to cloud-based and background agents, allowing an agent running in a web browser, a CI runner, or a backend service to access external tools over HTTP without needing a developer’s machine running local child processes.

A Step-by-Step Guide to Feature Flag Implementation in CI/CD Pipelines | Harness Blog

Engineering teams often deploy code much faster than they can safely release new features to users. This gap can create risks if releases skip testing, approvals, or gradual rollouts. Feature flags help by separating deployment from release, so you can ship code continuously and control which features users see through configuration.

Why Model Routing Backfires and How to Build Agents That Don't Burn Your Budget

Model routing promises to cut your AI agent spend by offloading routine tasks to cheaper models like Claude Haiku while reserving frontier models like Claude Sonnet for complex reasoning. In the right configuration, routing strategies can reduce inference costs by 40–85%. But if you implement routing incorrectly in a multi-turn agent, you can end up paying more than if you’d never routed at all. Here’s why and how to fix it.