Tag: AI observability
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 ...
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 ...
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 ...
AI-Driven Performance Testing: A New Era for Software Quality
Akash Thakur | | AI agents, AI defect detection, AI in DevOps, AI load testing, AI observability, AI performance testing, AI testing tools, continuous assurance, intelligent performance systems, LLMs in testing, ML-driven QA, next-gen performance testing, performance automation, performance engineering, performance engineers, performance lifecycle, performance monitoring, performance optimization, predictive performance analysis, predictive testing, requirements as code, self-healing systems, software reliability, SRE automation, testing transformationDiscover how AI and large language models (LLMs) are revolutionizing performance testing—shifting from reactive load testing to predictive, continuous assurance powered by intelligent agents and automation ...
The Future of Observability: Predictive Root Cause Analysis Using AI
Karthikeyan Uthirapathy | | AI in DevOps, AI observability, AI-driven RCA, AI-powered monitoring, anomaly correlation, anomaly detection, artificial intelligence, automated incident response, cloud environments, devops, distributed systems, intelligent observability, kubernetes, logs metrics traces, machine learning, microservices, observability tools, Predictive Analytics, predictive monitoring, predictive root cause analysis, proactive observability, rca, self-healing systems, system reliabilityIn the past few years, systems have become more complex than ever. Microservices, Kubernetes, cloud environments and distributed application programming interfaces (APIs) have changed how we build and manage software. However, this complexity has also made it harder ...
Observe Adds Two AI Agents to Improve Observability
Mike Vizard | | AI debugging, AI in DevOps, AI observability, AI SRE Agent, AIOps, code instrumentation, developer productivity, devops automation, incident triage, MCP Server, Model Context Protocol, observability platform, OpenAI Codex, OpenTelemetry, SRE automation, Telemetry dataObserve Inc. introduces the AI SRE Agent and o11y.ai Agent to its observability platform—empowering DevOps teams to automate incident triage, generate OpenTelemetry code, and query application performance using natural language for faster, ...
OpenTelemetry and AI are Unlocking Logs as the Essential Signal for “Why”
Bahubali Shetti | | agentic AI, AI incident response, AI observability, AI-driven observability, cloud native telemetry, cross-signal correlation, ECS, Elastic Common Schema, KubeCon + CNC NA 2025, LLM log parsing, log analysis, log normalization, MELT stack, OpenTelemetry, OTel logs, OTel semantics, OTLP, root cause analysis, schema standardization, SRE workflows, telemetry pipelinesLogs reveal the “why” behind failures. Learn how OpenTelemetry and AI transform raw log data into structured, actionable insights for modern observability ...
The Agentic AI-Driven Future of Telemetry
Perry Correll | | 10x investigators, agentic telemetry, AI infrastructure, AI observability, AI reasoning, AI scalability, AI-driven operations, AI-first architecture, autonomous agents, context fusion, contextual telemetry, human-in-the-loop, KubeCon + CNC NA 2025, MELT stack, metrics events logs traces, observability platforms, OCSF, OpenTelemetry, schema-agnostic architecture, telemetry normalizationTelemetry is evolving from passive data to AI-grade fuel. Learn how agentic telemetry fuses human and machine context to power self-healing, intelligent systems ...
The Breakneck Future of Codegen: Why AI SWE Must Be Matched with AI SRE
Anish Agarwal | | AI automation, AI code generation, AI in DevOps, AI infrastructure, AI observability, AI operations, AI reliability, AI SRE, AI-Driven Development, cloud-native systems, code duplication, distributed inference, GitHub Copilot, incident management, parallel reasoning, resilience engineering, site reliability engineering, software reliability, software vulnerabilities, technical debtAI codegen is transforming software development — but as speed and complexity increase, so does fragility. AI for site reliability will need to keep pace to avoid system breakdown and engineer burnout. ...
