Tag: AI observability
The Missing Runtime for Long-Running AI Agents
Anuj Kapoor | | agent governance, agent orchestration, agent runtime, AI agents, AI observability, AI resiliency, Azure AI Foundry, Azure Durable Functions, devops, distributed systems, durable AI agents, durable workflows, Enterprise AI, human-in-the-loop, long-running AI agents, multi-agent systems, platform engineering, stateful AI, workflow orchestrationEnterprise AI agents need more than stronger models. They need durable execution environments that can coordinate multi-step workflows, survive failures, pause for human review and resume reliably after disconnects or delays. AI ...
Automated Diagnosis Isn’t Automated Understanding: What Postmortems Teach Us About Building Trustworthy Incident AI
Jyostna Seelam | | AI incident response, AI observability, AI reliability, AIOps, alert correlation, automated incident response, causal reasoning, dependency graphs, devops, Engineering leadership, human-in-the-loop, incident diagnosis, incident management, LLM operations, non-deterministic systems, on-call engineering, postmortems, RAG systems, root cause analysis, SREAI incident tools can reduce alert noise, but real root-cause diagnosis requires causal reasoning, live dependency context, uncertainty handling and strong postmortem data ...
Preparing Infrastructure for the Next Phase of Agentic AI
Gretchen Stewart | | agent orchestration, agentic AI, AI agents, AI data architecture, AI infrastructure, AI modernization, AI observability, AI resource management, AI workflows, AI workload management, autonomous agents, cloud infrastructure, data movement, federal agencies, government AI, government IT, human-in-the-loop, hybrid infrastructure, infrastructure modernization, multi-agent systems, on-premises AI, public sector AIAgentic AI is changing government infrastructure requirements, pushing agencies to rethink workflows, observability, data movement and resource prioritization before investing in new hardware ...
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 ...
