Contributed Content
The Future of DevOps Still Has a Pulse
Anshul Sharma | | agentic DevOps, AI (Artificial Intelligence), AI Governance, AI infrastructure, devsecops, kubernetesOver the last few years, we have watched our industry get swept up in the promise of AI agents. The pitch is compelling: tell a system “Deploy this workflow and roll back ...
The Coding Assistant Bottleneck: Applying the 3-Ways
Eric Minick | | AI coding assistant, AI in CI/CD, CI/CD, developer productivity, devops, lifecycle managementAI coding assistants are the talk of the town, and for good reason. Tools that can turn a comment into a function or autocomplete entire classes are genuinely changing the developer experience ...
What Fuels AI Code Risks and How DevSecOps Can Secure Pipelines
Nitin Garg | | AI code risks, AI code security, devsecops, SBoM, secure pipeline, zero-trust securityModern development teams are under constant pressure to deliver fast, innovate continuously, and stay clear of security threats; all at the same time. Every new feature, every accelerated release, carries the hidden ...
How to Escape the Talent Valley
Dustin Lehr | | ai, AI Governance, AI in software development, application security, developer careers, devsecopsAcross the tech industry a disconcerting trend is emerging, job losses at the hand of a seemingly more efficient and cost-effective employee, artificial intelligence (AI). Software developers in particular have felt the ...
How to Build Engineering Teams That Drive Outcomes, not Outputs
Catherine McGarvey | | customer and business outcomes, devops, Engineering best practices, engineering silos, outcome-based teams, Product and designOutcome-based engineering teams focus on delivering real customer and business impact, aligning goals, adapting quickly, and collaborating across roles to drive results ...
Modular & Shift-Left Observability for Modern DevOps Pipelines
Shift-left observability makes monitoring a modular, built-in part of DevOps—improving reliability, cost efficiency, and visibility across modern cloud-native systems ...
The Hidden Cost of “Free” Open Source Infrastructure
John Amaral | | accountability, Modern container security, modern software development, Open Infrastructure, Sustainable stewardship, transparencyWhen the OpenSSF, PyPI, Rust Foundation, and OpenJS recently declared that "Open Infrastructure Is Not Free," they highlighted a crisis that affects every organization building modern software. Behind every container image pulled, every vulnerability scan and every automated ...
Scaling AI the Right Way: Platform Patterns for Performance and Reliability
Sandeep Bharadwaj Mannapur | | AI DevOps, AI observability, AI performance engineering, AI pipeline optimization, data ingestion performance, distributed training, elastic training, end-to-end AI performance, GPU utilization, inference latency, low-latency inference, MLOps automation, model caching, model serving optimization, performance bottlenecks in AI, platform engineering for AI, scalable AI systems, streaming ingestion, training throughputAI performance breaks long before the model runs. Learn how ingestion speed, elastic training, low-latency inference, observability and automation create reliable, scalable AI systems ...
Why AI Integration in DevOps is so Important
Andrej Kovacevic | | AI cybersecurity tools, AI DevSecOps, AI in DevOps security, AI pipeline monitoring, AI threat detection, AI-powered code analysis, automated vulnerability scanning, behavioral threat analysis, CI/CD security automation, DevOps anomaly detection, machine learning security, malware detection AI, predictive security analytics, real-time DevOps protection, secure DevOps pipelinesAI is transforming DevOps security with real-time threat detection, automated scanning and predictive analytics. Learn how AI strengthens CI/CD pipelines and protects modern software delivery ...
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 ...
AI Agent Performance Testing in the DevOps Pipeline: Orchestrating Load, Latency and Token Level Monitoring
Sudhakar Reddy Narra | | AI agent debugging, AI agent load testing, AI agent performance testing, AI CI/CD, AI DevOps, AI observability, AI orchestration metrics, AI-specific alerts, context utilization monitoring, context window saturation, cost-per-interaction, first token latency, inference tracing, LLM cost control, LLM cost optimization, LLM integration testing, LLM token throughput, model orchestration performance, production AI reliability, synthetic conversation testing, token analytics, token-rate monitoring, TPOT, TTFTTraditional testing misses token and context failures. Discover how to measure, test and scale AI agents reliably in production ...
MCP — A Protocol for SREs
Jaimin Jashwantlal Gandhi | | agentic AI in financial services, AI agents, AI observability, AI workflows, AI-powered DevOps, Azure MCP server, building MCP servers, DevOps AI tools, Docker MCP server, infrastructure as code AI, integrated development environment AI, LLM context management, LLM interoperability, MCP, MCP architecture, MCP servers, Model Context Protocol, remediation automation, SRE automationThe Model Context Protocol (MCP) standardizes how AI agents access tools, APIs and data. Learn how SREs can leverage MCP to build smarter, automated workflows ...
