AI
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 ...
AlertD Emerges to Apply AI to Observing AWS Environments
AlertD this week emerged from stealth to launch a DevOps platform that leverages generative artificial intelligence (AI) to provide deeper insights into Amazon Web Services (AWS) environments by automatically generating dashboards that ...
Tessl Details Plan to Build Platform for Managing AI Agents Embedded in DevOps Workflows
Tessl today outlined a plan to extend a registry it created for specifications that govern the behavior of artificial intelligence (AI) agents being used to build software to create a platform for ...
SRE in the Age of AI: What Reliability Looks Like When Systems Learn
Muhammad Yawar Malik | | adaptive systems, AI observability, AI reliability, concept drift, data pipeline reliability, feedback loops, incident response, learning systems, ML operations, MLOps, model drift, model performance monitoring, reliability engineering, site reliability engineering in AI, SREAs AI and ML become core production components, SRE is evolving from managing deterministic systems to ensuring the reliability of dynamic, learning systems. New metrics, workflows, guardrails and cross-disciplinary practices are redefining ...
New Relic Enhances Azure Integration with AI-Powered Observability Tools
Jon Swartz | | agentic telemetry, AI agents, AI observability, azure, gartner, github, MCP, Microsoft Fabric, Microsoft Foundry, Microsoft Visual Studio, New Relic, SAPNew Relic Inc. unveiled a suite of intelligent observability integrations with Microsoft Azure on Tuesday to streamline incident response and boost developer productivity as enterprises rush to adopt artificial intelligence (AI) workflows ...
I Saw DevOps’ Future—and Its Name is AI Native Dev
I was down in Brooklyn this week attending AI Native DevCon, and let me tell you—there’s no hidden metaphor or clever subtext in that name. What GuyPo and the gang have built ...
The Deterministic Future of AI-Generated Code
Yechezkel Rabinovich | | AI code generation, AI code review, AI development workflows, automated pull requests, behavioral verification, CI pipeline, code validation, deterministic guardrails, DevOps AI, ebpf, generated code risks, observability, smarter linters, software reliabilityAI has eliminated the bottleneck of writing code—but introduced massive uncertainty in verifying it. This piece explores why deterministic guardrails, smarter linters, and eBPF-driven observability are becoming essential to code review and ...
AWS Extends Kiro AI Tool to Generate Higher Quality Code
Amazon Web Services (AWS) today with the general availability of its Kiro artificial intelligence (AI) coding tool added additional capabilities, including a version that can now be invoked via a command line ...
Survey Sees AI Coding Creating Need for More Software Engineers
Mike Vizard | | agentic AI, AI compliance challenges, AI in DevOps, AI in software development, AI tools adoption, AI-generated code vulnerabilities, developer upskilling, DevOps productivity, DevSecOps challenges, GitLab DevSecOps survey, platform engineering, SDLC automation, software engineer demand, technical debt from AI, vibecoding risksA GitLab survey of 3,266 DevSecOps professionals shows AI is boosting code creation but increasing the need for skilled engineers, compliance challenges and human oversight ...
How Hyperconnected AI Development Creates a Multi-System Secret Sprawl
Guillaume Valadon | | AI development security, AI secret leaks, API key sprawl, Claude Code security, Cursor IDE risks, database credential leaks, GitHub secret exposure, hyperconnected AI workflows, large language model security, leaked API keys, MCP configuration files, MCP security risks, mcp.json leaks, OWASP AI testing, secret scanning toolsIf you've been building artificial intelligence (AI) tools lately, you've probably noticed something: Your development workflow has become incredibly connected. Tools such as model context protocol (MCP) sit at the center of it all, acting as the ‘brain’ that orchestrates how your large language ...
