Operations | Monitoring | ITSM | DevOps | Cloud

AI has changed how software gets built. It hasn't changed how software gets run. Most of the AI money in software has gone into the IDE: code generation, copilots, developer assistants, faster pull requests. That work matters. But writing software is one slice of the lifecycle. The harder problem, and the more expensive one, is running that software in production. Production is where systems fail in ways nobody predicted. Incidents don't stay inside one service.

PagerDuty fires. You open the incident. Title, timestamp, nothing else. Whatever context exists is in someone's head, in a Slack thread from two weeks ago, or in a runbook nobody has touched since the last reorg. We got tired of that. So we put an AURA agent behind a Slack bot and pointed it at our own production environment.

The open-source landscape has witnessed another highly automated, ecosystem-level subversion. On June 17, 2026, a critical software supply chain attack struck the Mastra AI framework - a popular open-source TypeScript ecosystem used widely to build AI agents, workflows and RAG pipelines. By exploiting a compromised contributor account, threat actors successfully mass-published 144 malicious packages under the official @mastra npm scope.

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.

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.

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.

This is the third post in our DRIVE Deep Dive series. We've covered Delivery and Reliability so far; this post takes on Initiatives. For the complete model, download the full DRIVE framework.

AI cost observability is the practice of measuring, attributing, and analyzing AI workload costs at the request, model, and workflow level in real time. It connects cloud infrastructure spend, inference and token costs, and business attribution (cost per feature, team, customer, or product) so engineering, finance, and product teams can see where AI spend goes and whether it creates value. On July 14, IBM had its worst trading day since 1987.

AI cost observability is the practice of measuring, attributing, and analyzing AI workload costs at the request, model, and workflow level in real time. It connects cloud infrastructure spend, inference and token costs, and business attribution (cost per feature, team, customer, or product) so engineering, finance, and product teams can see where AI spend goes and whether it creates value.