Operations | Monitoring | ITSM | DevOps | Cloud

Resolve's Agents of IT podcast - Ep. 11 - Sean and Ari's Hot Takes #itautomation #agenticai

Agentic AI is moving fast, but expectations are moving faster. In this episode of Agents of IT, Resolve CCO Sean Heuer and Ari Stowe, Resolve COO, cut through the noise around agentic AI, AIOps, and automation in modern IT environments. They react to recent articles from Forbes, TechCrunch, and others to unpack what’s real, what’s hype, and what actually works today.

Designing an automated SDLC control

For anyone shipping software in regulated industries, the word “control” gets thrown around all over. Compliance frameworks demand controls, auditors verify controls are used, engineering teams implement controls, and there are even Control Owners. But what exactly is a control? And more importantly, how do we design controls that actually serve their intended purpose while enabling rather than hindering delivery velocity?

How to Reduce Service Desk Workload with AI and Automation

For many IT directors, the service desk feels permanently stretched. It’s a math problem that is forever in motion. Every quarter brings new apps, new devices, new access rules, and new ways for small issues to become daily interruptions. Even when tooling improves, the queue still grows because the work expands with the environment. The pressure shows up in familiar places, like rising ticket counts, tighter SLAs, and a large backlog of projects that need help.

Why ITOps Automation Is Hard, Until You Change Your Approach

Automation fails in ITOps because it’s treated as a local efficiency gain rather than a system-level change—an approach that breaks down at scale as AI raises the bar for context, ownership, and control. Modern ITOps environments are hybrid, distributed, and assembled from overlapping vendors and platforms. Services run across clouds and teams. Signals arrive continuously. Dependencies change faster than they can be documented.

Why Infrastructure Stability Is Critical for Reliable DevOps Pipelines

Automation in DevOps helps teams move code from a commit to production faster. But it only works when the infrastructure is reliable and consistent. If servers fail, configurations drift, or scaling behaves unexpectedly, even a well-built pipeline can break. Stable infrastructure is what lets teams deploy many times a day with confidence instead of spending hours fixing failed releases. Often, the biggest difference between strong DevOps teams and struggling ones is how dependable their infrastructure is for continuous delivery.

Why AI Automation for ITOps Needs Context Graphs

AI automation in ITOps fails because execution loses decision context, and context graphs turn incident history into durable execution memory that systems can actually reuse. AI automation for ITOps fails because it remembers what it did, but not why. Fixing an issue depends on what was tried last time, what failed, what worked, which exceptions were approved, and under what conditions. That information rarely lives in the system.