Contributed Content
The Hidden Cost of AI Code: Keeping Quality Up With Production
AI accelerated code delivery, but the bigger change is in who's building software, how much is being built and what it takes to trust it. ...
AI Is Accelerating DevOps, Poor Integrations Are Slowing It Down
As AI speeds up software delivery, the real bottleneck isn't scanning or CI. It's how safely and predictably change moves across tools, teams, and companies. Something strange is happening in DevOps right ...
Why the Software Development Tools you Choose Directly Affect Your CI/CD Reliability
The software development tools you choose shape your CI/CD pipeline reliability in ways that only surface months later. Learn what to evaluate before adoption. ...
Agentic DevSecOps: AI Security Co-Pilots for Your CI/CD Pipeline
Joydip Kanjilal | | agentic AI, AI copilots, AI Governance, AI incident response, AI pipeline management, AI security, AI security agents, AI threat detection, AI vulnerability scanning, AI-driven security, AI-powered DevSecOps, application security, autonomous AI agents, autonomous security testing, CI/CD security, Cloud Security, continuous security monitoring, cybersecurity automation, devops security, devsecops, devsecops automation, infrastructure security, intelligent automation, real-time threat intelligence, SDLC security, Secure software delivery, security automation, security operations, security orchestration, security posture management, software development lifecycle, software security, threat remediationThe emergence of AI has brought endless possibilities and innovative opportunities in today’s ever-changing, fast-paced technology landscape. AI is helping development teams produce software significantly faster than ever before. AI-enabled DevSecOps tools ...
Risk-Based Review for Infrastructure as Code Pull Requests
Naga Krishna Reddy Muppidi | | automated review systems, blast radius analysis, change approval workflow, change management, CI/CD governance, cloud infrastructure, Cloud Security, deployment risk, deployment safety, devops, devsecops, Engineering Governance, engineering productivity, gitops, IaC review, infrastructure as code, infrastructure auditing, infrastructure automation, infrastructure change tracking, infrastructure compliance, infrastructure controls, infrastructure delivery, Infrastructure Governance, infrastructure observability, infrastructure reliability, infrastructure risk scoring, infrastructure security, operational risk management, platform engineering, platform operations, platform team workflows, policy as code, production changes, production deployments, pull request review, reviewer fatigue, reviewer workflows, risk assessment, risk-based review, security review automationNot every infrastructure pull request deserves the same review path. A tag change in a development account and a network-policy change in production should not create identical reviewer load. When every change ...
The Death of the Four Golden Signals: Designing Telemetry for Non-Deterministic Infrastructure
Jyostna Seelam | | AI architecture, AI compliance, AI Governance, AI guardrails, AI infrastructure, AI monitoring, AI observability, AI operations, AI operations strategy., AI performance metrics, AI platform monitoring, AI reliability, AI resilience, AI safety, AI SLIs, AI SLOs, AI system reliability, AI telemetry, AI trust, Enterprise AI, generative AI monitoring, guardrail intervention rate, hallucination detection, LLM monitoring, LLM observability, modern SRE, non-deterministic systems, Observability Engineering, OpenTelemetry, platform engineering, production AI, RAG observability, RAG systems, semantic drift, site reliability engineering, SRE, time to first token, token throughput, TTFT, vector database monitoringIn complex software systems, our traditional definition of operational health has always been comfortably binary. For over a decade, site reliability engineering (SRE) teams have relied on the industry-standard ‘Four Golden Signals’ ...
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 ...
