Operations | Monitoring | ITSM | DevOps | Cloud

AI agents are part of the modern development workflow. They write code, review pull requests, generate tests, call tools, interact with MCP servers, and help developers move faster. But behind every useful agent, there is something just as important as the model itself: the context that tells the agent how to behave. That context can include skills, prompts, instructions, hooks, commands, scripts, references, and MCP server definitions. In a small project, managing these primitives is pretty simple.

Edwin AI and Red Hat Ansible Automation Platform help ITOps teams move from correlated alerts and root cause analysis to governed, auditable remediation. When an outage starts, the first alert is only the first artifact. The harder work follows: grouping related signals, separating symptoms from cause, identifying the affected service, and deciding whether the next action is safe to run.

The original case for multicloud still holds — but the conditions around it have shifted significantly. Here's what you should be asking before your next cloud decision. Most organizations didn’t set out to have a multicloud strategy. They just accumulated one – a mix of providers, tools, and integrations built up over time as individual workload decisions compounded into something that now requires a strategy of its own.

A new report from LeadDev and Harness makes one thing clear: AI coding tools have fundamentally changed how much code organizations are producing. What has not changed nearly fast enough is how that code gets released. The State of AI-Driven Software Releases 2026 report, based on responses from 500424 engineers across industries and company sizes, puts real numbers behind a problem that engineering leaders have been feeling for a while. AI is accelerating the code creation side of the SDLC.