Operations | Monitoring | ITSM | DevOps | Cloud

In this episode of Grafana's Big Tent, hosts Mat Ryer (Senior Director of AI, Grafana Labs) and Tom Wilkie (CTO, Grafana Labs) sit down with Eric Burns, Field Executive Architect at Anthropic, to talk about building trust between tech and business execs, why Anthropic bet early on running across every major cloud, and what it was like watching large language models go from "interesting" to "obviously the future" in real time.

An AI agent is only as useful as the job you can safely hand it. In this Zero Ticket Minute, Ian Coppock, Resolve Customer & Partner Marketing Manager, breaks down why enterprise AI is moving toward purpose-built agents with defined roles, scoped permissions, and real guardrails. That is the foundation for autonomous IT operations and Zero Ticket IT. Subscribe for weekly insights on AI, IT automation, and where enterprise operations are heading.

AI wrote the code, but when something breaks, that's still on you. Code ownership matters more than ever.

It's time to go from reactive firefighting to predictive prevention. Learn how the Time Series Foundation Models can forecast system failures before they cascade.

Most enterprise AI agents take weeks to build. This one takes minutes. Watch how Agent Lab creates purpose-built agents with natural language, adds reusable skills, and sets guardrails before anything ships. From idea to production-ready in a single sitting.

AI adoption is easy to report. Business impact is harder to prove. Engineering leaders are under pressure to show what AI is actually changing — not just who is using it, but whether it is improving delivery, quality, developer experience, and business outcomes. This discussion between 3 engineering leaders explores how to move beyond vanity metrics, build a practical measurement approach, and communicate AI’s value to executives and CFOs with more credibility and less hype.

New: launch a Claude Code or Codex CLI session straight from the Agents panel. One click → isolated worktree → setup commands run → agent starts. Your other worktrees don't even notice. Parallel AI agents without the chaos. That's code flow.

In this video, you'll learn how to integrate Large Language Models (LLMs) with Site24x7 using Bring Your Own Key (BYOK), Zoho Key Services (ZKS), and Microsoft Azure OpenAI. Discover how Zia helps you analyze outages, understand performance issues, identify root causes, and get monitoring insights using simple natural-language queries. What you'll learn.

Highly classified environment. Strict compliance requirements. Data that can't leave the network. But still a real need for the competitive advantage AI delivers. Civo Director of Enterprise Cloud Solutions John Dietz addresses exactly that challenge and how Konstruct makes it possible to run Kubernetes, deploy your own models, and point Claude Code at your own internal private servers instead of public APIs.

AI incident assistant from BigPanda gives L2, L3, and SRE teams instant answers to resolve incidents faster without manual triage or tool-switching. IT teams lose critical minutes during incidents because context is scattered across Slack threads, bridge calls, monitoring tools, and historical tickets. The BigPanda AI Incident Assistant fixes that by surfacing relevant knowledge exactly when and where responders need it. It gives responders evidence-based resolution paths drawn from historical incidents and live system data, without leaving your workflows.

AI Incident Prevention from BigPanda stops change-related outages before they occur by leveraging risk scores, trend analysis, and guided remediation steps. Manual IT changes are still a leading cause of IT outages and disruptions. BigPanda AI Incident Prevention addresses this by automatically scoring change requests against historical data, flagging high-risk changes before they go live, and surfacing the recurring problems that cause service degradation.

On this episode of Masters of Data, we revisit the predictions Adam White, Zoe Hawkins, and David Girvin made at the end of last year, checking our own scorecard halfway through 2026. The hits: agents running amok and deleting databases, MCP becoming the backbone for tracking what agents actually do, growing security gaps around personal data, and a collective rejection of low-quality AI content. The misses: we underestimated how fast companies would cut staff for AI, then quietly start rehiring once the agents couldn't cover the work, and we're still arguing about whether token burn is a cost problem or a coming attack vector.

Claude Code + CircleCI MCP = no more hunting through build logs.

Let’s be honest: traditional automation breaks the second it hits a scanned PDF, a messy email thread, or an architectural drawing. Rules-based RPA simply lacks the cognition required to decode unstructured data. In this episode of, Project Manager Swetha K J breaks down exactly how we conquered this massive roadblock on our automation journey. By embedding advanced AI models directly into automation workflows, we’ve built a context-aware architecture that transitions systems from static execution to dynamic intelligence.

Watch the full episode: MCP vs CLI: Does it even make a difference? Live Laugh Logs ep. 3.

When AI could only answer questions, a bad prompt just meant a bad answer. But now AI agents read your documents, browse websites, and actually do things on your behalf. So when someone sneaks a malicious instruction into a file or a webpage, the agent doesn't just say something wrong. It does something wrong!

MCP vs CLI: does it even make a difference? Here’s everything you need to know. Welcome to Episode 3 of Live Laugh Logs, the podcast from the Coralogix Developer Relations team. This week Andre has made the move to the US, so Annie and Lewis are joined by George Pickers, Head of Solution Engineering for EMEA & APAC at Coralogix.

When AI writes the code and something breaks, nobody knows what it was supposed to do.

AI agents write code fast, but without a validation layer, fast just means faster bugs. In this video, we connect Claude Code to the CircleCI MCP server so Claude can trigger pipelines, pull build failures into context, and iterate until everything is green. No context switching. No copy-pasting logs.

The Aiven MCP connects Claude, Cursor, and VS Code to Apache Kafka. Inspect topics, track consumer lag, stream a database in with CDC, and manage your cluster. AIVEN DATA PLATFORM The Aiven Platform is more than a collection of open source services for streaming, storing and analyzing data. The platform ensures that all services run reliably and securely in the clouds of your choice, are observable, and can easily be integrated with each other and with external 3rd party tools.

Watch the full episode: MCP vs CLI: Does it even make a difference? Live Laugh Logs ep. 3.

Building AI agents is easy until they launch into production and start behaving unpredictably. In this presentation, João Freitas, Chief AI Officer at PagerDuty, dives into the messy reality of scaling non-deterministic systems and shares how PagerDuty manages multi-agent complexities. Speaker: João Freitas, Chief AI Officer, PagerDuty Recorded during GenAI Community x Google Developer Group Lisbon at PagerDuty Portugal offices, July 2026.

Logic App costs can be tricky - connectors, triggers, executions, and the consumption vs. standard plan decision all affect what you pay. Turbo360's Logic App Cost Review agent gives integration teams an instant AI-powered breakdown of cost patterns, what's driving the bill, and whether you're on the right plan. What you'll see.

Handed over an Azure integration environment you've never seen before? Turbo360's AI Resource Summary agent gives any support operator or engineer an instant plain-English overview of what a resource is, how it behaves, and what to watch out for - without needing to ask the developers. In this demo: Great for: IT operations teams, MSP NOCs, cloud support engineers, and anyone responsible for running integration workloads they didn't build.

Your UI changes, but your monitoring shouldn't break. Checkly's new agentic checks test the outcome you care about instead of running hard-coded steps. The main question is, "Can a user still do X?".

Same repo. Same tasks. Same validation gates. The only difference? Where validation happened. Here's how Chunk sidecars returned feedback in 22 seconds versus about 69 seconds through a traditional CI pipeline, giving the agent enough time to catch the failure, fix it, and revalidate before pushing.

AI agents write broken code nearly 50% of the time. By adding a traffic-based deterministic evaluation, Speedscale boosted unsupervised bug-fixing quality from 51% to 77% in just 5 minutes. This helped slash token costs and eliminate rework without human intervention. Learn more: speedscale.com.

Autonomous IT starts now with LogicMonitor and Edwin AI, built to help IT teams monitor complex hybrid IT environments, discover root cause faster, reduce downtime, and prevent incidents before they impact revenue or brand reputation. See how LogicMonitor brings AI-powered IT operations, observability, and incident prevention together for modern infrastructure teams.

At any given moment, your AI systems can be down, slow, overloaded, or just returning bad results. Someone's gotta babysit the bots. Sentry traces across your AI pipeline, from user request to final response, so you can see exactly what's happening and fix it.

AI Orchestrations analyzes historical event and incident data, including past responder behavior, and surfaces actionable recommendations for event orchestration rules that reduce noise, improve triage, and lower MTTR — so teams can spend more time innovating and less time fighting fires.#AIOps.

PagerDuty's agent app shows live incident state, incident history and change correlations inside GitHub so you can get context right within your PR without interrupting your flow. Automatically correlate incident data with recent commits and deployments to identify root causes, then generate fix PRs with proper incident linking.#IncidentResponse.

95% of Companies See No AI ROI. Here's Why | AI Tool Sprawl Explained 88% of companies are using AI, yet 95% report no measurable ROI. In this short from Agents of IT, we discuss why AI tool sprawl, poor workflow design, and lack of orchestration may be preventing enterprises from realizing the value of AI.

Enterprise AI adoption is accelerating, but are organizations actually solving business problems or just adding more tools? In this episode of Agents of IT, Fran Fernandez (Chief Product Officer at Resolve) and Zach Austin (Director of Product Marketing) explore one of the biggest challenges facing enterprise IT in 2026: AI tool sprawl. They discuss why many organizations struggle to demonstrate ROI from AI investments, how disconnected AI assistants create operational complexity, and why orchestration, automation, and context have become the real differentiators for enterprise AI success.

Today, we're launching Autonomous Worker Agents, AI agents that run as governed pipeline steps inside Harness. They inherit OPA policies, RBAC, audit trails, and scoped credentials from the first run. And because they live inside your Harness pipelines, they reason using the Harness Knowledge Graph: your services, deployments, incidents, and policies.