Operations | Monitoring | ITSM | DevOps | Cloud

Onboarding a new client shouldn't mean rebuilding the same dashboards by hand. Live demos show how our MCP server (in preview) hands that work to AI, built for MSPs, but relevant to any team automating dashboards and reporting.

Turns out telling coding agents what to do over and over again doesn't actually make them better at fixing CI failures.

AI can write 1,100 lines of code for a loading spinner in five minutes. Your change board still meets once a week. Karthik Jayaraman, VP of Information Technology at Fiserv, explains what happens when code generation speeds up and everything downstream stays the same. He covers why "human in the loop" doesn't scale, why handing all review to AI backfires, and why an agent told not to touch production needs a boundary it can't cross, not just an instruction.

DORA's data shows that as AI adoption goes up, individual effectiveness rises, and so does software delivery instability: more rollbacks and more unplanned rework. Nathen Harvey of Google's DORA team explains why AI acts as an amplifier of whatever system you already have. He walks through the seven capabilities that separate teams getting real gains from teams drowning in downstream chaos. He also argues that the risks stopping you from shipping AI-built work should become your platform roadmap.

Join Serge for an introduction to tracing and how it applies to AI agents. Through live walkthroughs, you’ll see how to follow an agent’s execution, inspect model and tool calls, and find where things went wrong. No prior tracing experience is needed.

Join Ribbon experts for a 30-minute preview of Network X Vienna and discover how AI, automation, and IP & Optical innovation are helping service providers simplify operations and prepare for the next generation of networks.

AI investigation for IT operations finds the likely root cause of an incident and shows the evidence behind it. This video covers how deep AI investigation works for ITOps, SRE, and platform engineering teams using LogicMonitor's Edwin AI. Engineers lose time stitching together alerts, logs, and recent changes after an alert fires. Deep AI investigation hands that first pass to AI agents, so your team starts closer to the fix. Edwin AI, LogicMonitor's AI agent for ITOps, correlates alerts, identifies root causes, and recommends remediation.

Everyone online seems to have a fully autonomous SDLC. Most enterprises are still at level 3, and they'll be there for years. In the EVOLVE 2026 opening keynote, Cortex co-founders Anish Dhar (CEO) and Ganesh Datta (CTO) lay out a practical maturity model for getting from AI coding agents to an AI software factory without blowing up cost, quality, or security along the way. They've watched hundreds of engineering orgs adopt AI over the last two years. This talk distills what separated the successful rollouts from the chaotic ones.

This video walks through what it looks like to treat Sentry's Seer agent the way you already treat a chatbot, asking it plain language questions about your own production application. From Slack or inside your Sentry account, Seer Agent can answer questions with real context: the affected spans, the recent release, the likely cause. Seer doesn't stop at answering questions. Ask it to act on what it just told you and it completes that work inside Sentry, serving as an assistant to your projects, team workflows, and your future self.

An Alibaba-affiliated AI agent went full crypto bro. During reinforcement learning, the agent autonomously started mining cryptocurrency, downloaded the tools it needed, and created a reverse SSH tunnel to get around network restrictions. What starts as a funny story about an AI vaping and mining crypto gets a lot more serious when you realize how sophisticated the behavior actually was.

On today's episode, Andrew Hillier chats with Sachin Gharge, Head of Cloud Platform at Scandinavian Airlines (SAS). They discuss AI, agents, Kubernetes, MCP, and optimization. Sachin shares how he and his team are optimizing cloud costs, leveraging automation, and experimenting with agentic AI, including bots and Slack integrations, to make operations easier and more effective for developers and the business.

Jev is a new AI model from Type Safe built around a very different idea: instead of generating long answers, it makes fast, simple decisions. The team reportedly even demoed it playing Doom using nothing but split-second choices. After years of teaching AI models to talk, could the next breakthrough be teaching them to simply decide?

What happens when an AI agent can do more than suggest a fix — and actually take action inside your CI pipeline? See Harness Worker Agents in action as an AI agent identifies a failed CI build, determines what went wrong, creates the fix, and gets the pipeline moving toward production again. Worker Agents bring AI-powered reasoning directly into your software delivery pipelines while maintaining the controls enterprises need, including sandboxed execution, scoped credentials, policies, and RBAC.

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.

Small agent changes mean a small test scope. The agent knows exactly what it changed, so it can pull the 50 tests that matter out of a 10,000-test inventory and skip the rest. Arjun Iyer on why that's the validation model that scales.

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.

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.

One question, ninety minutes: Do you know what AI agents you have running, what they're accessing, and what's their ROI? ServiceNow product leaders open with an overview of why legacy controls can't keep pace with AI agents. Then we'll showcase customer stories from Zespri and Booking.com, end-to-end demos of AI Control Tower, and a look inside ServiceNow's own AI estate.

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.

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In this video I go over what GitBench is, how to read its results, and some of the interesting things we can see in the results themselves. Chapters: For some reading on GitBench check out these two blog posts.

Honeycomb AI Ecosystem shows you the performance and cost of every AI agent in one live view and drills straight to the conversation behind any number, so you can diagnose your whole fleet in minutes without switching tools or rebuilding a single query.

See how BigPanda Analytics turns IT operations analytics into instant answers, no hand-built reports required. IT leaders are used to waiting on a report someone on the ops team had to hand build, and every follow-up question starts the cycle over again, while the business keeps moving. This demo shows how IT executives get direct access to IT operations analytics: prebuilt dashboards, AI-powered insights, and a natural language research tool that answers questions in real time.

In just two minutes, learn how our AI-powered platform unifies control across Windows, Mac, Linux, Mobile, and even VR/XR headsets. Discover how to eliminate tedious tasks with automated patching, zero-touch onboarding, and self-healing capabilities—allowing your team to focus on strategy instead of firefighting. What you’ll see in this video: How to achieve full visibility across your entire environment. The benefits of risk-based patching and continuous compliance. Proactive user experience optimization powered by AI. How to reduce risk and scale IT efficiency on a single platform.

We are stoked to announce that we have released our very own hosted MCP that allows you to use natural language to interact with and control anything you have running on Cycle.io. Alexander Mattoni, CTO and co-founder of Cycle, shares a few scenarios where the MCP can be useful. With the MCP, Cycle users can communicate with the Cycle platform directly from their preferred AI tool. In minutes, users can provision a new server, deploy an application, or troubleshoot an issue directly from their AI assistant like Claude, Claude Code, ChatGPT or Codex.

Aiven and SFEIR walk through what a governed, AI-ready foundation for product data looks like, and what it actually takes to build one. We'll be joined by Céline Thooris, Managing Director of WEnvision (SFEIR group), and Stan Dmitriev, Product Director for Aiven Context, sharing a first-hand view from the field and a concrete next step. What to expect.

With the plan mode you can get a preview what kind of panels you want to have in your dashboard that safe a lot of tokens, efforts and computing resources when using Grafana Assistant AI.