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AppDynamics and IBM Unlock Mainframe Transactions

Today’s digital businesses rely upon a large amount of software systems built throughout the history of the organization. Much of the focus to date has been on systems of engagement such as mobile and web applications, but there are countless systems underneath these newer modern applications.

As applications themselves evolve to the next computing paradigm, whether that be voice, virtual, or augmented reality, enterprises will build these new applications on new infrastructures, spanning hybrid cloud computing. Based on the 2017 Gartner CIO Survey, 44% of IT spending will be on digital by top performers in 2018. Yet shifting additional revenue to digital introduces risk. IDC estimates that unplanned downtime costs businesses between $1.3 – 2.5 billion per year.

AppDynamics has focused on these new applications, which are paramount to digital transformation. Enterprise customers leverage AppDynamics to monitor, troubleshoot, and gain insights into their businesses. The goal is always to improve visibility across all components critical to their digital businesses. Oftentimes, heritage systems such as IBM z Systems mainframes remain critical to enterprises. Examples of this are most prevalent in industries such as finance, insurance, healthcare, and government services. One example of this is card transactions — more than $6 trillion in card payments are processed annually by mainframes.

IBM and AppDynamics already have a strong partnership across a number of different areas, and now we’re thrilled to announce that this partnership is taking the next step by offering deep product integration between both companies. Together, we’re working to integrate IBM’s OMEGAMON Application Performance Management product with AppDynamics to provide transaction visibility into the mainframe.

This product integration extends the visibility of AppDynamics’ Map iQ and Diagnostic iQ into mainframe subsystems such as CICS and DB2, allowing for faster problem identification and isolation from a single end to end transaction path. This integration leverages the most popular monitoring platform on mainframe, built by the creator of the mainframe. This provides the much-needed visibility to the teams managing today’s most complex and mission critical digital business channels into a core technology of these businesses, the mainframe. This data sharing will facilitate collaboration between these often siloed organizations, and enable DevOps teams to understand the mainframe dependencies and performance. This is the deepest partner-driven integration by AppDynamics to date, and there are many exciting plans to continue collaborating to drive this new product offering forward.

The partnership between AppDynamics and IBM will be swiftly followed by a public beta of this product integration. The team looks forward to inviting mutual customers to use this integration and provide feedback during the development and general availability of this product offering.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.

AppDynamics and IBM Unlock Mainframe Transactions

Today’s digital businesses rely upon a large amount of software systems built throughout the history of the organization. Much of the focus to date has been on systems of engagement such as mobile and web applications, but there are countless systems underneath these newer modern applications.

As applications themselves evolve to the next computing paradigm, whether that be voice, virtual, or augmented reality, enterprises will build these new applications on new infrastructures, spanning hybrid cloud computing. Based on the 2017 Gartner CIO Survey, 44% of IT spending will be on digital by top performers in 2018. Yet shifting additional revenue to digital introduces risk. IDC estimates that unplanned downtime costs businesses between $1.3 – 2.5 billion per year.

AppDynamics has focused on these new applications, which are paramount to digital transformation. Enterprise customers leverage AppDynamics to monitor, troubleshoot, and gain insights into their businesses. The goal is always to improve visibility across all components critical to their digital businesses. Oftentimes, heritage systems such as IBM z Systems mainframes remain critical to enterprises. Examples of this are most prevalent in industries such as finance, insurance, healthcare, and government services. One example of this is card transactions — more than $6 trillion in card payments are processed annually by mainframes.

IBM and AppDynamics already have a strong partnership across a number of different areas, and now we’re thrilled to announce that this partnership is taking the next step by offering deep product integration between both companies. Together, we’re working to integrate IBM’s OMEGAMON Application Performance Management product with AppDynamics to provide transaction visibility into the mainframe.

This product integration extends the visibility of AppDynamics’ Map iQ and Diagnostic iQ into mainframe subsystems such as CICS and DB2, allowing for faster problem identification and isolation from a single end to end transaction path. This integration leverages the most popular monitoring platform on mainframe, built by the creator of the mainframe. This provides the much-needed visibility to the teams managing today’s most complex and mission critical digital business channels into a core technology of these businesses, the mainframe. This data sharing will facilitate collaboration between these often siloed organizations, and enable DevOps teams to understand the mainframe dependencies and performance. This is the deepest partner-driven integration by AppDynamics to date, and there are many exciting plans to continue collaborating to drive this new product offering forward.

The partnership between AppDynamics and IBM will be swiftly followed by a public beta of this product integration. The team looks forward to inviting mutual customers to use this integration and provide feedback during the development and general availability of this product offering.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.