Tag: AI observability
Production-Grade AI Eval Systems. What I Learned Putting LLMs on Call
Anjaiah Methuku | | AI evals, AI guardrails, AI observability, AI quality monitoring, AI reliability engineering, AI SRE, AI testing, answer relevance, CI evals, context precision, context recall, faithfulness, hallucination detection, human-in-the-loop, LLM evaluation, LLM judges, LLM reliability, model monitoring, production AI, production-grade AI, RAG evaluation, RAG metrics, regression testing, retrieval-augmented generationProduction-grade AI reliability requires more than uptime and latency. A layered eval system helps teams detect hallucinations, RAG failures and quality regressions before customers do ...
Reducing MTTR: A Practical Guide to Correlating Incidents with AIOps
Neel Shah | | AI for SRE, AI observability, AI-driven incident correlation, Aiden AI Copilot, Aiden for SRE, AIOps, alert correlation, alert fatigue, alert storms, automated remediation, automated root cause analysis, devops, Distributed Tracing, grafana, incident correlation, incident management, incident response, Jaeger, Loki, mean time to resolution, metrics logs traces, mttr, observability, ObserveNow, on-call automation, pattern-based correlation, Prometheus, root cause analysis, service dependency mapping, SRE automation, telemetry correlation, topology-aware correlationAI-driven incident correlation helps SRE and DevOps teams reduce alert noise, identify root causes faster and improve MTTR by connecting related metrics, logs and traces ...
What You Cannot See Will Break Your LLM App: A Practitioner Guide to Production Observability
Gourav Singla | | AI monitoring, AI observability, AI quality monitoring, automated AI evaluation, Distributed Tracing, finish reason, hallucination monitoring, LLM cost observability, LLM latency, LLM monitoring, LLM observability, LLM tracing, model-graded evaluation, OpenTelemetry GenAI, OTel GenAI, production AI reliability, production LLMs, prompt failures, prompt regression, provider monitoring, quality drift, token budget alerting, token cost monitoringTraditional application observability was built around a simple mental model: Your code runs, metrics come out and when something breaks, the logs tell you why. Large language models (LLMs) break that model ...
So Agentic Systems Are Messing Up Your SLO Framework
Saqib Jan | | agent monitoring, agentic AI reliability, Agentic Systems, AI agent SLOs, AI infrastructure, AI observability, behavioral correctness, behavioral drift, error budgets, inference reliability, nondeterministic AI, platform engineering, Probabilistic Systems, probabilistic tracing, semantic similarityTraditional SLOs cannot show whether AI agents are behaving correctly. Platform teams need layered metrics for infrastructure, inference and behavioral reliability ...
From Reactive Monitoring to AI-Driven Operational Intelligence
Sneha Gullapalli | | AI observability, Amazon Q, Apache Iceberg, automated root cause analysis, AWS CloudWatch, AWS operations, CloudWatch Logs, DevOps observability, Distributed Tracing, Five Whys, GenAI observability, incident response, LLM monitoring, log lake, MTTR reduction, operational intelligence, proactive monitoring., RAG latency, S3 Tables, token usage monitoringTraditional monitoring often meant chasing alerts and toggling between dashboards after an issue had already impacted users. AWS CloudWatch — long the backbone of metrics, logs and traces on AWS — is ...
The Death of the Four Golden Signals: Designing Telemetry for Non-Deterministic Infrastructure
Jyostna Seelam | | AI architecture, AI compliance, AI Governance, AI guardrails, AI infrastructure, AI monitoring, AI observability, AI operations, AI operations strategy., AI performance metrics, AI platform monitoring, AI reliability, AI resilience, AI safety, AI SLIs, AI SLOs, AI system reliability, AI telemetry, AI trust, Enterprise AI, generative AI monitoring, guardrail intervention rate, hallucination detection, LLM monitoring, LLM observability, modern SRE, non-deterministic systems, Observability Engineering, OpenTelemetry, platform engineering, production AI, RAG observability, RAG systems, semantic drift, site reliability engineering, SRE, time to first token, token throughput, TTFT, vector database monitoringIn complex software systems, our traditional definition of operational health has always been comfortably binary. For over a decade, site reliability engineering (SRE) teams have relied on the industry-standard ‘Four Golden Signals’ ...
Grafana Labs Extends Observability Reach Deeper Into AI
Mike Vizard | | AI agents, AI observability, cloud observability, devops, Grafana 13, Grafana Assistant, Grafana Labs, GrafanaCON 2026, kafka, kubernetes, Loki, Model Context Protocol, OpenTelemetry, Telemetry dataGrafana Labs debuts Grafana 13, a specialized AI application observability platform, and an MCP-powered AI agent at GrafanaCON 2026 to streamline telemetry across complex cloud-native environments ...
How Much Is That AI Subscription in the Window?
Adrian Bridgwater | | Agentic Workflows, AI agents, AI Infrastructure Costs, AI observability, AI Subscription Models, Anthropic, API Billing, ChatGPT Pro, Claude Code, Codex, Context-Heavy Development, Developer-Tooling, Enterprise IT Spending, Future of AI Inference, Machine Learning Monetization, OpenAI, software lifecycle engineering, Sonnet 4.6, Tech Pricing Volatility, Token Usage LimitsAn analysis of the escalating AI subscription wars between Anthropic and OpenAI, highlighting the "Single Prompt Sinkhole" phenomenon where power users exhaust $100/month limits in hours and the industry's shift toward observability ...
What to do About AI’s Forced Rethink of Reliability in Modern DevOps
Leo Vasiliou | | Agentic Workflows, AI monitoring, AI observability, AI reliability, AI. analytics, business impact, CDNs, customer churn, devops, digital experience, experience-driven resilience, incident response, internet stack, KPIs, latency, LLM applications, model drift, monitoring tools, NPS, observability, performance, performance degradation, reliability, reliability strategy, revenue, SaaS, site reliability engineering, SLIs, SLOs, SRE Report 2026, Third-party Dependencies, trust, uptime, user experienceAs systems become more distributed and AI-driven, traditional uptime metrics are no longer enough. The 2026 SRE Report shows how reliability is shifting toward user experience, speed, and business impact, and how ...
From Automation to Autonomy: What AIOps Actually Looks Like Today
Ankush Dhar | | AI for IT operations, AI incident management, AI observability, AI runbook automation, AIOps, autonomous operations, DevOps automation limits, incident triage AI, MTTR reduction, operational automation AI, support ticket deflectionFor years, engineering leaders have been promised that automation would shrink operational work. CI/CD pipelines, runbooks, chatbots and DevOps tooling were supposed to mean reduced tickets, fewer incidents and fewer 3 a.m ...
Real-Time Anomaly Detection: Integrating Log Service With Agentic AI Pipelines
Neel Shah | | agentic AI DevOps, AI observability, AI-driven incident response, autonomous DevOps pipelines, cloud observability logs, DevOps anomaly detection, Isolation Forest anomaly detection, Kubernetes anomaly detection, LangChain agents, MLOps monitoring, MTTR reduction, OpenTelemetry monitoring, real-time anomaly detection, self-healing systemsLearn how agentic AI and real-time anomaly detection create self-healing DevOps pipelines. This guide covers architectures, code examples, and metrics to cut MTTR by up to 90% ...
Why Your AI Agent Strategy is Failing (and How to Fix It): The Microservices Playbook for AI Agents
Lokhesh Ujhoodha | | agentic AI, AI agent transparency, AI agent trust, AI agents, AI architecture, AI Governance, AI infrastructure, AI microservices, AI observability, AI ROI, autonomous agents, CIO AI strategy, Enterprise AI, enterprise AI strategy, LLM adoption, microservices architecture, monolithic AI agentsDespite billions in AI investment and countless vendor promises, most enterprises are still treating AI agents like glorified copilots rather than autonomous systems. After working with numerous enterprise customers implementing AI agents across various ...
