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AppDynamics Announces Application Run Book Automation Capabilities

AppDynamics announced an Application Run Book Automation solution designed specifically to remediate business impact in modern applications within seconds.

IT Operations professionals have typically automated server tasks such as deploying operating systems and applying patches, but they have not automated tasks at the application layer due to the complexity of modern application environments with hundreds of distributed services and frequent release cycles. Without sufficient visibility into the application stack and the analytics to understand it, it has not been possible to create runtime dependencies and interactions between different application services.

AppDynamics now provides this capability by identifying and resolving business impact from an application perspective, using real-time analytics to detect problems so remediation can be automated using the right run book. It therefore unifies “monitoring” and “management” of application performance into a single product by using the core intelligence of the AppDynamics Pro product — which learns the baseline performance of an application—to know exactly when to spring into action and initiate a fix. This capability ensures that application support teams can reduce their Mean-Time-To-Resolution from minutes to seconds.

AppDynamics 3.7 ships with a library of run books that includes:

- Thread dump collection

- JVMs, IIS, and server restarts

- Ability to execute scripts from Chef or Puppet

- Ability to run existing run books or custom scripts

- Incident creation in service management consoles like ServiceNow and Jira

- Trigger of PagerDuty workflows

“We see Application Run Book Automation to be a bold and disruptive addition to an industry that’s more focused on ‘monitoring’ than on actually enabling users to manage and resolve problems instantly,” said Jyoti Bansal, AppDynamics’ Founder & CEO. “Our APM solution already equips customers with insight and analytics into their application—removing the ‘black box’ nature of their application—and now Application Run Book Automation gives them an ‘auto pilot’ capability to remediate problems without manual intervention. We’re excited about helping our customers reduce their mean-time-to-resolution even further than they do today.”

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AppDynamics Announces Application Run Book Automation Capabilities

AppDynamics announced an Application Run Book Automation solution designed specifically to remediate business impact in modern applications within seconds.

IT Operations professionals have typically automated server tasks such as deploying operating systems and applying patches, but they have not automated tasks at the application layer due to the complexity of modern application environments with hundreds of distributed services and frequent release cycles. Without sufficient visibility into the application stack and the analytics to understand it, it has not been possible to create runtime dependencies and interactions between different application services.

AppDynamics now provides this capability by identifying and resolving business impact from an application perspective, using real-time analytics to detect problems so remediation can be automated using the right run book. It therefore unifies “monitoring” and “management” of application performance into a single product by using the core intelligence of the AppDynamics Pro product — which learns the baseline performance of an application—to know exactly when to spring into action and initiate a fix. This capability ensures that application support teams can reduce their Mean-Time-To-Resolution from minutes to seconds.

AppDynamics 3.7 ships with a library of run books that includes:

- Thread dump collection

- JVMs, IIS, and server restarts

- Ability to execute scripts from Chef or Puppet

- Ability to run existing run books or custom scripts

- Incident creation in service management consoles like ServiceNow and Jira

- Trigger of PagerDuty workflows

“We see Application Run Book Automation to be a bold and disruptive addition to an industry that’s more focused on ‘monitoring’ than on actually enabling users to manage and resolve problems instantly,” said Jyoti Bansal, AppDynamics’ Founder & CEO. “Our APM solution already equips customers with insight and analytics into their application—removing the ‘black box’ nature of their application—and now Application Run Book Automation gives them an ‘auto pilot’ capability to remediate problems without manual intervention. We’re excited about helping our customers reduce their mean-time-to-resolution even further than they do today.”

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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 ...