Operations | Monitoring | ITSM | DevOps | Cloud

AI agents are quickly becoming the first responders on the most sophisticated SecOps and DevOps teams, pulling threads on a threat or making sense of a flood of telemetry before a human even logs in. But most AI pricing hasn’t caught up. Token-based models leave you burning through usage with no visibility into what’s driving the cost, while per-seat caps ration access like AI is still a nice-to-have instead of core to how your team operates.

For years, engineering teams have faced the same challenge during incidents: the systems generating alerts are becoming more complex, but the investigation that follows is still largely manual. Monitors are good at telling us that something is wrong. The harder question is why. When an alert fires, an engineer still has to work out where to look, which signals matter, what hypothesis to test, and how the evidence across logs and metrics fits together.

Security and operations teams are collecting more telemetry than ever, and AI is accelerating that curve. IDC projects the world will generate 393.9 zettabytes of data in 2028, up from 149 zettabytes in 2024, with AI and machine learning workloads driving much of that growth. That growth forces a hard trade-off. Ingest everything, and you pay for it. Filter aggressively, and you risk missing the signal that matters.

If you’ve spent any time digging for insights in logs, you know the shape of the problem. A single log line might contain an IP address, a status code, a response time, and a user ID, but it’s all buried in one long, unstructured string. You know the information is there. Getting it into a field you can filter, group, or chart on is a different matter.

“AI-ready” is everywhere right now, attached to nearly every product in every category. The catchy label rarely means anything specific, just as additional questions are warranted when vendors claim to be “AI-native”. After fighting through all the marketing jargon, there needs to be a standard, not a slogan. And the definition changes depending on what the data is for. AI-ready for a data warehouse and AI-ready for live operational telemetry are not the same problem.

We’ve recently shown how Mobot, Sumo Logic’s AI assistant, has evolved from a search assistant into a true thinking partner, introducing natural language to log analysis and monitors. Today, we’re bringing that same conversational reasoning to one of the most powerful parts of the platform: playbooks in Automation Service. Historically, playbook authoring has been a highly manual process that required deep organizational and Sumo Logic platform knowledge.

The new Mobot is a conversational AI assistant that helps teams explore and understand their data using natural language. We’re now extending those same capabilities to one of the most time-consuming tasks in observability: creating and configuring monitors.

Moving to the cloud is one of the most consequential decisions an IT organization makes. A successful cloud migration strategy sets the foundation for how your business scales, innovates, and competes. But too often, cloud migration initiatives stall, underperform, or force organizations to repatriate applications back on-premises because the groundwork wasn’t laid correctly.

AI is changing how teams work. Developers are generating code faster, security teams are automating investigations, and employees across the business are using AI tools to accelerate research, content creation, and decision-making. But this adoption comes with a catch. As usage explodes, it introduces a new set of security risks: a rapidly expanding attack surface, faster attack timelines, potential data exposure, and an alarming lack of visibility into how these tools are being used.

Every single day, the Sumo Logic Platform analyzes more than four exabytes of log data. The good news? The answers to your application performance, infrastructure health, and security incidents are hidden in those logs. The challenge? Historically, uncovering those answers required query language fluency. That’s why we built Mobot, our conversational interface that connects users to advanced AI capabilities using natural language.