Operations | Monitoring | ITSM | DevOps | Cloud

For enterprise IT leaders, the question is no longer whether AI agents can help run IT operations, but how to scale them beyond pilots and into the everyday work of keeping the business online. It’s no coincidence that this year’s Gartner IT Infrastructure, Operations & Cloud Strategies Conference (IOCS) opens with a keynote dedicated to scaling agentic AI to enable autonomous IT operations. At BigPanda, we’ve seen a shift from the ITOps leaders we work with every day.

The debate is no longer whether AI belongs in ITOps; it’s about how quickly it can be implemented, how to measure it, and who’s accountable when it acts. And although IT leaders across industries are scrambling to integrate agentic AI into their operations, those deployments are facing serious challenges. A new Harris Poll survey of more than 300 U.S. data and AI decision-makers puts a number on the problem.

On August 17, Gartner placed BigPanda in the Niche Players quadrant of the 2026 Gartner Magic Quadrant for AI Applications in IT Service Management (ITSM). It’s our first time on this MQ, after an honorable mention last year. I’m glad we’re there, and I want to explain why a company that doesn’t sell an ITSM platform reads that as a strong result.

It’s 2:14 am, and checkout latency at your largest retail store has tripled. Your observability platform’s agent has already posted to Slack that there is a connection pool exhaustion on the orders database, with a link to the trace. Your cloud provider’s agent reports a degraded network path in one availability zone. The AI SRE tool one of your product teams adopted last spring points to a deploy that went out at 1:50.

Downtime is expensive, and it’s getting harder to avoid. Every year, enterprise IT environments grow more distributed, dynamic, and difficult to maintain with legacy tools and manual processes. With unplanned outages costing enterprises nearly $25,000 per minute, establishing and maintaining a resilient IT infrastructure is essential. Enterprises have already invested heavily in resilient architecture.

A major incident can turn a highly skilled engineer into a coordinator, a search engine, and a status writer. The work looks necessary in the moment. The cumulative effect is easy to miss: the people most needed to understand the failure spend their time repeating what others already know.

Enterprise software can now change faster than most operating models can interpret what changed. That creates a question many AI plans leave unanswered: when development accelerates, where does the operational bottleneck go?

Most teams we talk to are fighting the same battle. The knowledge needed to make a decision already exists somewhere. However, it’s locked in a tool your team isn’t looking at, or in the head of the one engineer who’s seen this same type of incident before. This month’s updates all chip away at that same problem. Here’s what’s new.

The modern enterprise runs on a technology stack that changes faster than the operating model responsible for keeping it available. Applications that once moved through scheduled releases now change continuously. Infrastructure is distributed and dynamic. Services depend on other services, teams depend on other teams, and operational data arrives from more places than any individual can reasonably inspect. The business asked for agility, flexibility, and velocity, and the tech delivered.

When a major incident opens, the opening minutes often become a race across disconnected tools and competing theories. One engineer checks a monitoring tool. Another scrolls through change records, looking for the one line that explains everything. A third pings Slack, asking if anyone has seen this before. While these are reasonable steps, taken one at a time, in sequence, they are far too slow.