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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 ...
Observability: The Real Killer App
My friends, I’ve been around long enough in the world of DevOps, cybersecurity and cloud-native to see the “next big thing” fade into “just another dashboard.” But today I’m here to tell ...
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 ...
The Future of DevSecOps: From Shifting Left to Shifting Smart
For the better part of a decade, a revolutionary idea has reshaped enterprise security: the "shift left" movement. This mantra taught us to view the software development lifecycle (SDLC) not as a ...
Splunk Donates OpenTelemetry Injector Library to Open Source Instrumentation Framework
Splunk, an arm of Cisco, has donated an OpenTelemetry Injector library it developed for streamlining instrumentation of applications that have not been containerized, to the open source project that is being advanced ...
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 ...
Beyond the Cloud: A DevOps Blueprint for High-Performance Bare Metal Infrastructure
Ethan Vance | | bare metal servers, CI/CD build runners, DDoS protected hosting, dedicated servers for databases, DevOps infrastructure, ECC memory, hardware reliability RAID, high-performance computing, I/O intensive workloads, infrastructure as code DevOps, Kubernetes bare metal nodes, low-latency infrastructure, network redundancy, NVMe storage performance, Tier-1 bandwidth providersBare metal servers deliver predictable, high-performance compute, storage, and networking—ideal for databases, CI/CD runners, and Kubernetes workloads ...
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 ...
Terraform Users: Day 2 Operations Aren’t Failing Over Tools, Because Day 1 Never Happened
Eran Bibi | | cloud drift detection, Cloud Governance, cloud security posture, cloud system of record, Day 1 cloud visibility, day 2 operations, FinOps failure, IaC adoption, IaC coverage, infrastructure discovery, production visibility, shadow IT cloud, Terraform drift, unmanaged resourcesYour disaster recovery plan just failed — not because your DR strategy was wrong — but because you had no idea what you were actually running in production. Every Day 2 tools you've bought (think: FinOps, drift detection, ...
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 ...
