Tag: devops automation
AI Agents in CI/CD Pipelines: Speed vs Control in Modern DevOps
The moment you push your code, deployment fires off on its own. The pipeline kicks in, the tests sail through, and within a few minutes your app is live in production. There ...
GitHub Copilot Gets Its Own App — and It’s More Than a Coding Assistant
Tom Smith | | Agent Merge, agentic development, AI coding assistant, CI/CD, developer tools, devops automation, GitHub Copilot, software development lifecycleGitHub has launched a standalone Copilot desktop app in technical preview, moving beyond the IDE to introduce a dedicated cockpit for agentic development. The app enables developers to manage parallel, isolated agent ...
GitHub’s Spec Kit Puts the Spec Back in Software Development
Tom Smith | | AI coding agents, devops automation, GitHub Copilot, GitHub Spec Kit, software governance, Spec-Driven Development (SDD), technical debt, vibe codingGitHub’s open-source Spec Kit aims to eliminate "vibe coding" by prioritizing durable specifications over vague prompts, providing a structured, agent-agnostic workflow for Copilot, Claude, and Gemini ...
Cursor’s New SDK Turns AI Coding Agents Into Deployable Infrastructure
Tom Smith | | agent control plane, AI coding agents, autonomous pull requests., CI/CD agent pipelines, Cloud execution sandboxing, Composer 2 model, Cursor SDK, devops automation, MCP integration, Model Context Protocol, Multi-agent workflows, Programmatic code generation, semantic search indexing, software lifecycle engineering, TypeScript SDK for AICursor's TypeScript SDK lets teams invoke AI coding agents programmatically from CI/CD pipelines, with sandboxed VMs, MCP support, and token-based pricing ...
AIOps Isn’t Optional Anymore: What Modern DevOps Teams Must Adapt To
Michael Chukwube | | AIOps, devops automation, incident response, machine learning operations, observabilityAIOps is becoming essential for DevOps teams, enabling faster incident response, less alert noise and improved reliability at scale ...
Cursor Cloud Agents Get Their Own Computers — and 35% of Internal PRs to Prove It
Tom Smith | | agent-driven development, AI code review, AI coding agents, autonomous software development, CI/CD and AI agents, cloud-based AI development, Cursor cloud agents, devops automation, self-verifying AI code, software engineering automationCursor’s new cloud AI coding agents can build, test, and verify software autonomously using real UI interaction. With 35% of production PRs generated by agents, software delivery is shifting from code authoring ...
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 ...
Bot-Driven Development: Redefining DevOps Workflow
Oluwakorede Akinsete | | agentic AI, AI collaboration, AI in DevOps, AI-powered tools, automation challenges, AWS CodeGuru, bot-driven development, CI/CD pipeline, continuous integration, devops automation, DevOps productivity tools, GitHub Copilot, hybrid model, intelligent agents, operational efficiency, Security in Devops, software lifecycle, workflow reinventionExplore the future of DevOps with bot-driven development (BotDD) that integrates AI-powered tools. Learn how intelligent agents enhance automation, testing, and project management, transforming the software lifecycle ...
Making CI/CD Pipelines Truly Autonomous — Safe and Observability-Driven Workflows With TypeScript, Python and Contract-First API Testing
Sapan Tiwari | | API contract testing, autonomous CI/CD pipelines, CI/CD observability, contract-first API testing, devops automation, devops best practices, pipeline orchestration, Python DevOps automation, Self-Healing Pipelines, TypeScript contractsLearn how to design safe, observable, and autonomous CI/CD pipelines using contract-first API testing, TypeScript for contract safety, Python for orchestration, and observability-driven decision making ...
The Self-Inflicted Outage: When “Too Big to Fail” Meets the Reality of Hyperscale Complexity
Michael Smith | | automation risk, cascading failures, certificate lifecycle management, cloud outage causes, cloud reliability, cloud resilience, devops automation, distributed systems reliability, failure design, hyperscaler failures, infrastructure as code, infrastructure complexity, multi-cloud resilience, operational risk, PKI automation, scaling limits, self-inflicted outages, SRE best practicesModern cloud outages are increasingly caused by automation, configuration errors, and hidden design limits. Learn how to build resilience ...
Three Strategies for Winning the AI Race With DevOps
Marty Puranik | | AI deployment speed, AI DevOps, AI in DevOps pipelines, AI observability, AI pipeline efficiency, AI resource management, AI-driven DevOps strategies, cost-efficient GPU hosting, devops automation, distributed training, faster model training, GPU hosting for AI, GPU infrastructure, HPC for AI, MLOps optimization, model optimization, scalable AI workflowsAI is transforming DevOps. Learn how faster model training, optimized pipelines and smarter GPU infrastructure help teams deliver reliable, scalable AI workflows ...
GitOps in the Wild: Scaling Continuous Delivery in Hybrid Cloud Environments
Ankur Mahida | | continuous delivery, devops automation, drift detection, GitOps architecture, GitOps at scale, GitOps best practices, GitOps case study, GitOps challenges, GitOps governance, GitOps observability, GitOps security, hybrid cloud GitOps, hybrid cloud operations, infrastructure as code, Kubernetes delivery, multi-cloud DevOps, Open Policy Agent, policy as codeA deep dive into scaling GitOps for hybrid and multi-cloud environments, exploring architecture, governance, security, observability and real-world enterprise practices ...
