Contributed Content
From AI Hype to AI Assurance: How Engineering Teams Can Safely Ship AI-Enabled Software
Olamilekan Lamidi | | AI assurance, AI Governance, AI testing, generative AI, MLOps, software engineeringAI has moved very quickly from experimentation to production. A few years ago, many organizations were still asking whether AI could improve their products or internal workflows. Today, the question is different: ...
Why Developer Workstations Have Become a Critical Part of the Software Supply Chain
Samuel Ogbonna | | CI/CD security, Developer Security, Developer Workstations, devsecops, open source security, Software Supply ChainFor years, software supply-chain security discussions focused on centralized infrastructure such as build servers, package registries, and CI/CD systems. Recent attacks suggest that this view is incomplete. The Megalodon campaign injected malicious ...
Multi-Model AI Debate: The New Way to Design Software Systems
How agentic workflows can automate architecture debates, compare tradeoffs and produce stronger software designs ...
How to Build a DevSecOps CI/CD Pipeline on Azure With GitHub Actions
Fix security problems when they’re cheap to fix, which is before the code is deployed. A pipeline that enforces this automatically is what makes that principle real ...
Shadow Mode Continuous Integration: The Missing Test Layer for AI Agents
Autonomous agents require CI/CD pipelines that evaluate behavior, not just artifacts. Containerized shadow mode gives platform teams a practical control point: Run the agent in an isolated Docker environment, replay realistic workflows, ...
From Alerts to Intelligence: Building a Production Self-Healing System for Port-Down Failures
Big, distributed computing systems seldom have visible failures. Most of them start without any bang, frequently with a health-check disconnection, a failed TCP connection or a service port that is no longer ...
Self-Healing Infrastructure With Cognitive Automation: How LLMs and Ansible Transform Middleware Reliability
Infrastructure reliability has always been central to enterprise success, yet traditional automation methods often fall short in handling complex, dynamic environments. With the rise of large language models (LLMs), a new paradigm ...
Building CI/CD Pipelines for On-Prem Azure DevOps: What the Cloud Docs Don’t Tell You
The engineering that makes on-prem CI/CD reliable is invisible when it works. When it does not work, the failures are subtle and the documentation is thin ...
Why AI-Driven Devops is Exposing the Limits of Traditional Toolchains and What Comes Next for Engineering Teams in 2026
The future belongs to adaptive systems that can learn, adjust and self-correct in real-time. Teams that invest in observability, modularity and AI-aware governance today will be positioned to thrive in this new ...
Developing Real-Time Event Processing With Micronaut and Kafka Streams: A Step-by-Step Guide
Learn how to develop lightweight, high-performance stream processing applications in Java with contemporary frameworks ...
How AI is revamping DevSecOps processes
Umashankar Kotturu | | AI DevSecOps, CI/CD, devsecops, policy as code, security automation, Software Supply ChainArtificial Intelligence is pushing DevSecOps into a new phase where security is no longer just about detecting vulnerabilities, but increasingly about resolving them automatically within the flow of software delivery. As many ...
How Independent Service Deployments Expose the Limits of Conventional Regression Testing Tools
The architectural shift to independently deployable services was supposed to make software delivery faster and less risky. In many aspects, it has. Teams can ship a change to one service without coordinating ...
