Contributed Content
The Silent Risk of AI-Written DevOps Pipelines
Sannan Ali | | AI-generated DevOps pipelines, CI/CD, devsecops, pipeline security, Software Supply ChainThese days, when a developer needs a CI/CD pipeline, they don’t always dive into GitHub Actions docs or spin up Jenkins from scratch. Instead, they pull up an AI assistant and type ...
Agentic Observability is Not a Chatbot Over Telemetry
Agentic observability isn’t about removing engineers from the loop. It is about making the loop faster, better informed, and easier to operate at the scale modern systems require. ...
Why DIY Test Automation Succeeds Its Way Into a Problem
Rohit Raghuvansi | | AI Development, DIY Test Automation, Quality Engineering, software testing, test automationAsk any engineering team if they can build their own test automation framework, and the answer is almost always “yes.” With modern AI tools involved, that answer arrives faster and with more ...
Regression Testing Tools in the Age of AI-Assisted Development: What Has Changed
Sophie Lane | | AI-assisted development, devsecops, regression testing tools, software testing, test automationFor most of the past decade, the conversation around regression testing tools was fairly stable. The tools got faster, the integrations got smoother, and the underlying approach stayed largely the same: write ...
Overcoming IP Churn in Ephemeral DevOps Environments Using Userspace Overlays
Modern DevOps practices have completely transformed how we handle compute and orchestration. Tools like Kubernetes enable engineering teams to spin up ephemeral containers in seconds and scale workloads dynamically to meet global ...
Why Enterprise AI Infrastructure Is Becoming a DevOps Problem
Most enterprise AI projects start with retrieval. You connect Jira, Confluence, SharePoint, and Slack. Maybe a few internal databases nobody has touched in five years. You tune embeddings, optimize chunking, wire up ...
Why Logs, Metrics and Traces Still Don’t Give You Real Observability
If your team can answer the question “Did the system do the right thing?” and not just “Did the system stay up?”, you’re getting close to real observability ...
Why Your AI Agent is a Black Box and How to fix it With OpenTelemetry
You built the agent. It works in testing. Then it hits production and starts giving wrong answers, timing out or burning through your token budget, and you have no idea why. This is ...
Agentic SRE: The Next Frontier of Reliability
Agentic SRE is the evolution of site reliability engineering where AI agents help observe systems, reason over telemetry and take bounded operational actions under human-defined guardrails ...
Why AI Won’t Solve the Hardest Part of Integrations
Michael Zuercher | | AI integrations, API management, integration platforms, operational complexity, SaaS infrastructureAI is making it easier for SaaS companies to build integrations. Give a coding agent decent API docs, some context about the systems involved, and a clear prompt, and it can get ...
More Signal, Less Clarity: The Observability Paradox No One Wants to Talk About
Record observability spending is driving up MTTR. Discover why tool sprawl and excessive dashboard data cause cognitive overload for on-call engineers, and how to fix it ...
Why Agent Skills Are the Next Evolution of Software Development
The emergence of agent skills — modular, reusable blocks of natural language instructions and metadata — is transforming the developer’s role ...
