Beyond reactive IT: Building proactive operational intelligence for the mainframe

When a mission-critical system slows down, IT teams are quickly flooded with information. Alerts start firing as dashboards and performance data show signs of trouble across the environment.

Figuring out what’s behind the slowdown can take much longer. The cause may sit somewhere else in the environment, perhaps with a late-running batch process or a recent configuration change. When multiple alerts fire around the same time, teams have to piece together how they’re connected and where the issue began.

Enterprises typically bridge that gap with experienced people who know how to interpret ABENDs, connect the dots across System Management Facility (SMF) reports, and determine what the data is telling them. This gets harder as applications stretch across different environments and generate more operational data along the way. 

Mainframe modernization is increasingly focused on helping teams make sense of the information already in front of them. That means identifying issues earlier, reducing downtime, and giving more people the context to make critical decisions without relying so heavily on a small group of specialists.

The gap between an alert and an answer

Enterprises have invested heavily in monitoring and observability. Operations teams aren’t short on information about system health and performance.

But an alert was never supposed to be a full answer. It simply tells an operator where to start looking for one.

When an incident begins, engineers must decide which signals matter and where to investigate first. A problem in an application may have originated in a batch workload, an infrastructure dependency, or a change made elsewhere. More telemetry doesn’t necessarily make that relationship obvious.

Experienced operators have traditionally supplied the missing context. They know which logs to check and recognize patterns from previous incidents. However, relying on that knowledge becomes less practical as environments span more systems and change more frequently.

That’s the difference between monitoring and diagnostic intelligence. One surfaces the problem, and the other helps teams understand it. AI can correlate operational data and identify relationships across systems, narrowing the field of investigation before a performance issue can escalate.

The cost of a slow diagnosis

The longer teams spend searching for a cause, the longer the business and impacted customers wait for resolution.

A performance issue can escalate into an outage, while a missed batch window can hold up a business process for hours. Application changes can also have unexpected effects when dependencies aren’t clear. By the time teams understand what happened, engineers from across the organization may already be pulled into the investigation just to figure out where the problem belongs. Meanwhile, engineers from multiple teams may be pulled into the investigation simply to determine who owns the problem.

That uncertainty also makes specialized expertise an operational bottleneck. An experienced operator might recognize a familiar pattern almost immediately. Someone new must reconstruct the same context from documentation tools and colleagues.

Knowledge transfer can help, but it can’t capture every judgment built through years of experience. A better opportunity is to bring that context into the investigation itself, so expertise isn’t limited to whoever is available when an incident occurs.

Putting intelligence where the work happens

Automation has removed a lot of manual work from IT operations. The difficulty lies in those where the response isn’t known.

Suppose an application suddenly slows down. An operator may have several alerts and thousands of lines of logs, but no obvious cause. Agentic AI can examine those signals alongside related events and system dependencies to narrow the possibilities. The operator remains in control and doesn’t have to assemble every piece of context manually first.

This approach can also help teams get ahead of problems. Before making a configuration change, they can understand the dependencies involved and spot potential effects earlier. That same context can help protect critical processing windows and give operators a starting point when unfamiliar issues arise, drawing on what the organization has learned from previous incidents.

That makes operational intelligence part of mainframe modernization. 

Modernization also extends to how teams understand and operate the systems running the business. Improving that day-to-day experience can be just as important as changes to applications and infrastructure.

From information to action

For proactive operations to work, intelligence must be available when teams decide what to do, not buried in another dashboard or disconnected tool.

Rocket EVA is one example of how that model can be applied. The agentic AI-powered platform works across mainframe operations to analyze information in context and support teams as they investigate issues and make changes.

Its applications span several parts of the environment:

• Operations and diagnostics: Correlate operational signals to identify likely root causes and shorten the path from alert to response.
• Knowledge and workforce resilience: Bring institutional knowledge into daily workflows, giving employees more context when they encounter unfamiliar issues.
• Batch operations: Surface scheduling, JCL, and workload dependencies so teams can address delays before they reach downstream processes.
• Applications and configuration: Show relevant dependencies and potential impacts before making changes, reducing avoidable surprises.
• Data-driven decision-making: Put trusted enterprise and operational information into context while preserving the security and governance that mission-critical systems require.

These give teams a better chance of doing something useful with it. That can mean fewer routine investigations landing on the desks of the most experienced specialists. It can give teams time to intervene while an issue is still manageable. Understanding dependencies before making a change can prevent the next incident from occurring in the first place.

A more proactive operating model

The mainframe earned its place at the center of critical business operations through reliability. The operating model around it should be held to a similar standard.

Incidents will still happen, and experienced people will still make difficult calls. But they shouldn’t spend the first part of every investigation piecing together information the organization already possesses.

That’s a practical opportunity for mainframe modernization: make operational intelligence available early enough to change the outcome. When teams can get from an alert to an explanation faster, they have more time to act before an IT problem becomes a business problem.

See how Rocket EVA helps turn mainframe operational data into actionable intelligence, giving teams the context to diagnose issues faster and act earlier.