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Rocket TMON Released

Rocket Software announced powerful new innovations to its Skills and Efficiency solutions, designed to help enterprises scale IT operations, close the IT skills gap and improve developer experience with intelligence and precision. 

New product features include automation, productivity-focused tools, and optional AI-driven capabilities, supporting faster development, stronger system performance, and greater IT resilience without adding layers of complexity and risk. By making it easier for developers and infrastructure teams to work more efficiently, the company continues to support businesses in their IT modernization initiatives and transformation journeys.

These latest innovations from Rocket Software empower customers to:

  • Boost developer efficiency by cutting task time from hours to minutes and accelerating new developer ramp-up from months to weeks.
  • Improve system performance through AI-driven monitoring and anomaly detection for greater reliability across mainframe environments.
  • Reduce IT workload with self-service automation to decrease ticket volume by up to 16%, all while maintaining compliance and security.
  • Strengthen resilience with advanced, point-in-time data recovery that minimizes downtime and protects mission-critical systems.

“IT teams are facing unprecedented demands to deliver more while balancing innovation with operational resilience,” said Phil Buckellew, President, Infrastructure Modernization Business Unit at Rocket Software.  “These advancements directly address today’s critical needs – closing the IT skills gap, improving operational efficiency, and enabling modernization without disruption. By aligning cutting-edge technology with business goals, we empower IT leaders to simplify their operations, accelerate business outcomes, and future-proof their organizations, without additional risk.”

Rocket Software’s approach to enabling IT modernization while reducing the risk of disruption is at the heart of its product development strategy.  These advancements reflect the company’s commitment to delivering customer value through innovation, evidenced by the introduction of new optional capabilities for both developers and infrastructure teams, including:

  • Rocket® TMON™: Proactively identifies mainframe performance issues and anomalies before they impact operations using AI-powered analytics, machine learning, and KPI measurement to proactively identify performance issues and anomalies before they impact operations.
  • Rocket® Zena™: Empowers non-technical users to automate processes independently, resulting in reduced reliance on IT intervention.
  • Rocket®  EDX: Makes document management and search faster and easier with natural language input, done via voice or text.
  • Rocket® Rapid Data Recovery: Reduces downtime through single point in time data recovery.
  • Rocket® MultiValue Developer Assistant: Streamlines the generation, autocompletion, and explanation of MV BASIC code, speeding up time to productivity for new developers from months to weeks.
  • Rocket® Uniface® Developer Assistant: Helps users navigate Uniface documentation, learn the platform faster, generate and explain ProcScript code, and enhance code clarity with comments and plain-language explanations.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Rocket TMON Released

Rocket Software announced powerful new innovations to its Skills and Efficiency solutions, designed to help enterprises scale IT operations, close the IT skills gap and improve developer experience with intelligence and precision. 

New product features include automation, productivity-focused tools, and optional AI-driven capabilities, supporting faster development, stronger system performance, and greater IT resilience without adding layers of complexity and risk. By making it easier for developers and infrastructure teams to work more efficiently, the company continues to support businesses in their IT modernization initiatives and transformation journeys.

These latest innovations from Rocket Software empower customers to:

  • Boost developer efficiency by cutting task time from hours to minutes and accelerating new developer ramp-up from months to weeks.
  • Improve system performance through AI-driven monitoring and anomaly detection for greater reliability across mainframe environments.
  • Reduce IT workload with self-service automation to decrease ticket volume by up to 16%, all while maintaining compliance and security.
  • Strengthen resilience with advanced, point-in-time data recovery that minimizes downtime and protects mission-critical systems.

“IT teams are facing unprecedented demands to deliver more while balancing innovation with operational resilience,” said Phil Buckellew, President, Infrastructure Modernization Business Unit at Rocket Software.  “These advancements directly address today’s critical needs – closing the IT skills gap, improving operational efficiency, and enabling modernization without disruption. By aligning cutting-edge technology with business goals, we empower IT leaders to simplify their operations, accelerate business outcomes, and future-proof their organizations, without additional risk.”

Rocket Software’s approach to enabling IT modernization while reducing the risk of disruption is at the heart of its product development strategy.  These advancements reflect the company’s commitment to delivering customer value through innovation, evidenced by the introduction of new optional capabilities for both developers and infrastructure teams, including:

  • Rocket® TMON™: Proactively identifies mainframe performance issues and anomalies before they impact operations using AI-powered analytics, machine learning, and KPI measurement to proactively identify performance issues and anomalies before they impact operations.
  • Rocket® Zena™: Empowers non-technical users to automate processes independently, resulting in reduced reliance on IT intervention.
  • Rocket®  EDX: Makes document management and search faster and easier with natural language input, done via voice or text.
  • Rocket® Rapid Data Recovery: Reduces downtime through single point in time data recovery.
  • Rocket® MultiValue Developer Assistant: Streamlines the generation, autocompletion, and explanation of MV BASIC code, speeding up time to productivity for new developers from months to weeks.
  • Rocket® Uniface® Developer Assistant: Helps users navigate Uniface documentation, learn the platform faster, generate and explain ProcScript code, and enhance code clarity with comments and plain-language explanations.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...