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tribe29 Releases Robotmk

tribe29 announces the first full release of “Robotmk” - developed by IT consulting company Elabit, Simon Meggle.

The new plugin offers an easy-to-deploy, open source option combining Application Performance Monitoring (APM) with observability into the underlying infrastructure.

Robotmk alerts IT administrators to application performance issues before they affect customers and end-user experience. The new plugin uses the popular open source Robot Framework to deliver synthetic testing. This simulates human interaction with web-based, cloud-native and traditional enterprise applications, providing continuous real-world performance monitoring.

The plugin’s deep integration with Checkmk makes it simple to configure automated alerts. Administrators can easily build and share custom dashboards that combine application performance data with key metrics from the underlying infrastructure. And, in the event of any issues, teams can drill-down into Checkmk’s broader monitoring to pinpoint root causes in the infrastructure.

“Up to now, this type of end-to-end monitoring and observability has been too complex or prohibitively expensive for many companies,” comments Jan Justus, CEO of tribe29. “This deep integration of two established open source tools not only offers a more affordable option, it takes speed and ease-of-deployment to new levels.”

Robotmk highlights problematic conditions in applications that would previously have gone undetected by traditional monitoring. Application owners and IT administrators can now share an overview of the optimal function and response times of their applications. Administrators can manage monitoring settings such as runtime thresholds granularly via rules in Checkmk, and even spin off individual sections of a test as separate monitoring services.

Checkmk's built-in graphing features also allow IT teams to draw conclusions about possible root causes. IT managers can precisely identify where delays or other limitations are occurring and initiate targeted countermeasures. This allows organizations to delegate the mitigation process to the appropriate teams, depending on the cause of the problem, and eliminate critical conditions faster.

"With Robotmk, companies can add automated software testing to their Checkmk monitoring; this takes service quality assurance to a whole new level," says Simon Meggle, CEO of Elabit. "The approach has already proven itself in practice and the feedback on Robotmk after the test phase has been consistently positive...after only a short time, not only companies but also the public sector have successfully implemented Robotmk."

The production release of the Robotmk plugin (version 1.1.0) is now available in the Checkmk Exchange.

Robotmk can be used with all Checkmk editions in version 1.6 and from 2.0.

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tribe29 Releases Robotmk

tribe29 announces the first full release of “Robotmk” - developed by IT consulting company Elabit, Simon Meggle.

The new plugin offers an easy-to-deploy, open source option combining Application Performance Monitoring (APM) with observability into the underlying infrastructure.

Robotmk alerts IT administrators to application performance issues before they affect customers and end-user experience. The new plugin uses the popular open source Robot Framework to deliver synthetic testing. This simulates human interaction with web-based, cloud-native and traditional enterprise applications, providing continuous real-world performance monitoring.

The plugin’s deep integration with Checkmk makes it simple to configure automated alerts. Administrators can easily build and share custom dashboards that combine application performance data with key metrics from the underlying infrastructure. And, in the event of any issues, teams can drill-down into Checkmk’s broader monitoring to pinpoint root causes in the infrastructure.

“Up to now, this type of end-to-end monitoring and observability has been too complex or prohibitively expensive for many companies,” comments Jan Justus, CEO of tribe29. “This deep integration of two established open source tools not only offers a more affordable option, it takes speed and ease-of-deployment to new levels.”

Robotmk highlights problematic conditions in applications that would previously have gone undetected by traditional monitoring. Application owners and IT administrators can now share an overview of the optimal function and response times of their applications. Administrators can manage monitoring settings such as runtime thresholds granularly via rules in Checkmk, and even spin off individual sections of a test as separate monitoring services.

Checkmk's built-in graphing features also allow IT teams to draw conclusions about possible root causes. IT managers can precisely identify where delays or other limitations are occurring and initiate targeted countermeasures. This allows organizations to delegate the mitigation process to the appropriate teams, depending on the cause of the problem, and eliminate critical conditions faster.

"With Robotmk, companies can add automated software testing to their Checkmk monitoring; this takes service quality assurance to a whole new level," says Simon Meggle, CEO of Elabit. "The approach has already proven itself in practice and the feedback on Robotmk after the test phase has been consistently positive...after only a short time, not only companies but also the public sector have successfully implemented Robotmk."

The production release of the Robotmk plugin (version 1.1.0) is now available in the Checkmk Exchange.

Robotmk can be used with all Checkmk editions in version 1.6 and from 2.0.

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...