Contributed Content
Context Engineering is the Key to Unlocking AI Agents in DevOps
Abhijit Ubale | | AI agents, automation, context engineering, context management, data governance, devops, incident management, kubernetes, memory architectures., Model Context Protocol, observability, operational complexity, performance optimization, production-grade agents, Productivity, scaling AI, selective context injection, structured memory, tool integration, workflow efficiencyExplore how context engineering is essential for transforming AI agents from experimental prototypes to reliable production tools in DevOps. Understand its impact on automation workflows, accuracy, and scalability ...
I Learned Traffic Optimization Before I Learned Cloud Computing. It Turns Out the Lessons Were the Same.
Harshit Omar | | cloud infrastructure, cloud optimization, cloud resilience, infrastructure portability, multi-cloud strategyI wanted to be a race car driver before I knew what a data center was. I started in traffic, not in the cloud. This was not a childhood dream driven by ...
The Future of AI in Software Quality: How Autonomous Platforms are Transforming DevOps
Igor Kirilenko | | agentic AI, CI/CD pipelines, devops automation, software quality, static analysisAgentic AI is transforming DevOps by embedding autonomous quality checks into CI/CD pipelines, improving code security, coverage and speed ...
How OPA Changed Our Go-No-Go Forever
Learn how Open Policy Agent (OPA) transformed go/no-go releases from subjective meetings into automated, auditable, policy-driven decisions embedded directly in the CI/CD pipeline ...
The Great Infrastructure Migration: Why Teams are Moving From Manual Provisioning to Autonomous Platforms
Neel Shah | | autonomous infrastructure, DevOps productivity, infrastructure automation, platform engineering, self-service infrastructureAutonomous infrastructure platforms replace manual provisioning, accelerating delivery, improving compliance and freeing platform teams to focus on strategic work ...
The Hidden Security Risks in Modern JavaScript Frameworks and How React Teams Can Reduce Them
Samuel Ogbonna | | client-side rendering security, client-side vulnerabilities, content security policy, Dependency Management, frontend attack surface, frontend security best practices, input validation, JavaScript dependencies, JavaScript security, JavaScript vulnerabilities, modern JavaScript frameworks, NPM security, React application security, React security risks, secure React development, SPA security, state management security, third-party libraries risk, web application security, XSS attacksReact and modern JavaScript frameworks accelerate development—but hide serious security risks. Learn where vulnerabilities come from and how to reduce client-side attack surfaces ...
GitOps Implementation at Enterprise Scale — Moving Beyond Traditional CI/CD
Sumanth Kadulla | | ArgoCD, change traceability, CI/CD migration, CI/CD reliability, cloud native delivery, configuration drift, declarative infrastructure, deployment automation, deployment rollback strategy, DevOps at scale, devsecops, dora metrics, gitops, GitOps adoption, GitOps security, infrastructure as code, Kubernetes continuous delivery, platform engineering, policy as code, pull-based deploymentsTraditional CI/CD pipelines hit scaling limits. Learn how GitOps improves deployment reliability, security, and DORA metrics—and what it takes to migrate successfully ...
When DevOps Meets the Cloud: A Real-World Transformation Story
Yashwanth | | CI/CD Automation, cloud migration, DevOps transformation, infrastructure as code, observabilityA real-world DevOps and cloud transformation story showing how automation, observability, and cultural change improved reliability and delivery ...
Why AI Makes Requirements a Runtime Artifact
Tom Hollowell | | AI behavior, AI Governance, AI requirements, continuous verification, MLOps, model drift, production monitoring, requirements traceability, runtime constraintsIn traditional software, requirements are static design-time artifacts. In AI-enabled systems, they must be continuously observed and enforced in production. Learn how AI collapses the boundary between design-time and runtime, shifting requirements ...
What to do About AI’s Forced Rethink of Reliability in Modern DevOps
Leo Vasiliou | | Agentic Workflows, AI monitoring, AI observability, AI reliability, AI. analytics, business impact, CDNs, customer churn, devops, digital experience, experience-driven resilience, incident response, internet stack, KPIs, latency, LLM applications, model drift, monitoring tools, NPS, observability, performance, performance degradation, reliability, reliability strategy, revenue, SaaS, site reliability engineering, SLIs, SLOs, SRE Report 2026, Third-party Dependencies, trust, uptime, user experienceAs systems become more distributed and AI-driven, traditional uptime metrics are no longer enough. The 2026 SRE Report shows how reliability is shifting toward user experience, speed, and business impact, and how ...
Test Automation Strategy for Growing Software Teams
Build a scalable test automation strategy that improves release speed, reduces defects, and supports growing software teams ...
What a ‘Good Plan’ Really Means for AI Coding Agents
Michael Parker | | Agile methodologies, AI coding agents, application delivery, CI/CD pipelines, coding efficiency, context retention, continuous improvement, Engineering best practices, error management, modernization, planning in development, software development, task execution, validation processesExplore how AI coding agents are transforming software development. This article discusses the importance of clear objectives, runnable tasks, and continuous validation in planning to ensure that the transition from idea to ...
