Operations | Monitoring | ITSM | DevOps | Cloud

The Return on Your Databricks Investment Lives in What You Run on It

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.

Mission-Critical Data Orchestration with Agentic AI | Automated SFTP, DataOps & Workflow Automation

How do you automate mission-critical data pipelines without risking downtime? In this Resolve Reels episode, see how Resolve's Agentic Automation Platform enables DataOps teams to build resilient, end-to-end workflows that automate secure SFTP transfers, preflight system validation, database operations, exception handling, intelligent retries, and self-healing remediation.

Never Touch Another IT Ticket Again | AI That Resolves IT Issues Automatically

What if your IT team never had to touch another password reset, VPN issue, or software request? This hilarious commercial imagines a world where IT tickets resolve themselves. See how agentic AI automates password resets, access requests, VPN troubleshooting, software installs, and more, so your service desk can focus on higher-value work instead of repetitive tickets. Resolve's AI-powered platform helps enterprises reduce ticket volume, improve first contact resolution, lower ITSM costs, and move toward Zero Ticket IT with autonomous resolution.

What if AI could resolve your IT tickets before they're ever created?

Watch how agentic AI automates password resets, VPN troubleshooting, access requests, software installations, and other repetitive IT service desk tasks without human intervention. Resolve helps enterprises reduce ticket volume, lower ITSM costs, improve employee experience, and move toward Zero Ticket IT. If you're researching AI for IT support, ServiceNow automation, ITSM automation, autonomous IT operations, or AI service desk solutions, this Short shows what's possible.

How Agentic AI Enables Autonomous Threat Response at Machine Speed

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.

Your AI isn't underperforming. Your data foundation is.

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.

Instrumenting AI Agents for the Agent Timeline: A Practical OpenTelemetry Guide

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.

Why Observability Isn't Enough for AI Coding Agents

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.