Operations | Monitoring | ITSM | DevOps | Cloud

We turned the mic around for this episode, with Zoe Hawkins interviewing Adam White and Jake Lee about Sumo Logic’s redesigned free trial. The new 14-day sandbox comes preloaded with realistic data feeds, Cloud SIEM, and our AI agents, so no one has to build collectors or open tickets before finding out whether the platform fits. Adam and Jake walk through the guided “choose your own adventure” paths, the broad, open-ended questions that bring out the best in Mobot, and how the trial will keep pace with new releases.

We use Sentry's new Regex search for Logs to hunt for non-obvious bugs in our app. What do you do when traces show that Postgres jobs are backing up? Logs are trace-connected, so by looking at one of the affected traces, we can see all of the logs on that trace. In there, we can see Postgres has logged that it acquired a lock only after a long wait. Using regex, we can find all of these logs that describe acquiring a lock after a 10-second-plus wait. That narrows our search from thousands or hundreds of logs down to about a dozen.

5 things you can do with the new Coralogix APM, no alerts to write and no database agent to install.

Thousands of agent sessions last month. But what did they actually ship? Vendor consoles show you tokens, Coralogix shows you impact, repo by repo, session by session. No vendor lock-in, no data limits.

Do you know how much of your AI agent spend goes to repos your company doesn't even manage? Olly makes it one question away.

Most teams keep an agent from regressing by hardcoding checks into its logic, an if-statement here, a regex there. Every new rule then becomes a code change, a review, and a deploy, and the person who spots the problem in production is rarely the person who can ship the fix. Agent Control moves those rules out of the code and into one hub. Steer, block, and validate agent behavior in real time, with rules any team member can update without touching the codebase.

Your agent breaks in production. You fix it and move on. But the input that actually broke it is gone and two months later the same failure quietly comes back, because there was never anything to test against. That's not a debugging problem. It's a missing dataset. This demo turns low-scoring production traces into a regression suite you can run against every prompt and model change, without writing a single test case by hand.

Every engineering team has unit tests. They tell you the code still works. They tell you nothing about what the model started saying. This demo wires a single eval gate script into a git pre-push hook, so Splunk Agent Observability scores every agent's output before the push is allowed through. Luna, an on-premise small language model, runs as a synchronous judge against fixed thresholds. Fail one, and the push is blocked.

Agentic AI is transforming the enterprise, but its success hinges on one critical factor: trust. Without robust governance, auditability, and a unified data foundation, scaling AI safely remains out of reach.