Operations | Monitoring | ITSM | DevOps | Cloud

Why do 40% of alerts received by security teams today go completely uninvestigated? It’s not due to a lack of concern but instead caused by shortening attack windows and compounded by overwhelming tech sprawl. Today’s security teams are operating in a threat landscape defined by escalating attacks, tighter budgets and mounting alert fatigue. Organizations process an average of 960 security alerts per day, and large enterprises handle more than 3,000 daily alerts across roughly 30 tools.

New research reveals why Australian businesses are entering the new financial year with bigger AI budgets and the same unsolved problem. One in three Australian businesses exceeded their AI budget last year. Yet, half of them plan to increase AI spending again this year. Yet the behaviour that caused those budget overruns remains largely unaddressed.

AI agents are nondeterministic, multi-step, and opaque. When one fails in production, "the model said something weird" is the cheapest, most useless line in your incident postmortem. To debug agents the way they actually run, you need telemetry that captures all of it, in order, with enough context to reconstruct what happened. The OpenTelemetry GenAI Semantic Conventions give you a vendor-neutral way to do exactly that.

Observability platforms collect pre-instrumented logs, metrics, and distributed traces to monitor production systems and surface failures to human engineers. The adoption of AI into engineering has led observability providers to offer those same signals to agents. This is often packaged as AI observability, but the signals themselves were designed around a human investigation loop. AI coding agents work faster, consume data differently, and need feedback as they work rather than after deployment.

Organizations are no longer asking whether to adopt AI; that question is settled. The focus now is on reaching a point where AI is doing meaningful operational work—or as the industry calls it, being “AI-first.” But being “AI-first” isn’t binary. You don’t go from zero AI to meaningful autonomy by flipping a switch. In reality, getting there means moving through distinct stages.

TL;DR Agentic observability uses AI agents to autonomously investigate incidents, identify root causes, and take action in production environments. Unlike traditional monitoring (which alerts and waits) or AIOps (which assists human analysis), agentic platforms conduct the investigation themselves. Key capabilities include autonomous incident triage, evidence-backed root cause analysis, alert noise reduction, and governed remediation.

Databricks built the most capable AI platform the enterprise has ever seen at Data and AI Summit 2026. The data on who actually earns a return from it tells a more sobering story. Here is what changed at the summit, and what it means for leaders already on the platform. Ten minutes into the Data + AI Summit 2026 keynote, Ali Ghodsi, CEO of Databricks, said something most enterprise leaders were not prepared to hear: AGI is already here.